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Journal articles on the topic 'Local clustering coefficient'
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1
Yu, Pei, Qiang Guo, Ren-De Li, Jing-Ti Han, and Jian-Guo Liu. "Roles of clustering properties for degree-mixing pattern networks." International Journal of Modern Physics C 28, no. 03 (March 2017): 1750029. http://dx.doi.org/10.1142/s0129183117500292.
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The clustering coefficients have been extensively investigated for analyzing the local structural properties of complex networks. In this paper, the clustering coefficients for triangle and square structures, namely [Formula: see text] and [Formula: see text], are introduced to measure the local structure properties for different degree-mixing pattern networks. Firstly, a network model with tunable assortative coefficients is introduced. Secondly, the comparison results between the local clustering coefficients [Formula: see text] and [Formula: see text] are reported, one can find that the square structures would increase as the degree [Formula: see text] of nodes increasing in disassortative networks. At the same time, the Pearson coefficient [Formula: see text] between the clustering coefficients [Formula: see text] and [Formula: see text] is calculated for networks with different assortative coefficients. The Pearson coefficient [Formula: see text] changes from [Formula: see text] to 0.98 as the assortative coefficient [Formula: see text] increasing from [Formula: see text] to 0.45, which suggests that the triangle and square structures have the same growth trend in assortative networks whereas the opposite one in disassortative networks. Finally, we analyze the clustering coefficients [Formula: see text] and [Formula: see text] for networks with tunable assortative coefficients and find that the clustering coefficient [Formula: see text] increases from 0.0038 to 0.5952 while the clustering coefficient [Formula: see text] increases from 0.00039 to 0.005, indicating that the number of cliquishness of the disassortative networks is larger than that of assortative networks.
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Meghanathan, Natarajan. "Local clustering coefficient-based assortativity analysis of real-world network graphs." International Journal of Network Science 1, no. 3 (2017): 187. http://dx.doi.org/10.1504/ijns.2017.083577.
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Meghanathan, Natarajan. "Local clustering coefficient-based assortativity analysis of real-world network graphs." International Journal of Network Science 1, no. 3 (2017): 187. http://dx.doi.org/10.1504/ijns.2017.10004296.
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Liu, Xiao-Lu, Shu-Wei Jia, and Yan Gu. "Empirical analysis of the user reputation and clustering property for user-object bipartite networks." International Journal of Modern Physics C 30, no. 05 (May 2019): 1950035. http://dx.doi.org/10.1142/s0129183119500359.
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User reputation is of great significance for online rating systems which can be described by user-object bipartite networks, measuring the user ability of rating accurate assessments of various objects. The clustering coefficients have been widely investigated to analyze the local structural properties of complex networks, analyzing the diversity of user interest. In this paper, we empirically analyze the relation of user reputation and clustering property for the user-object bipartite networks. Grouping by user reputation, the results for the MovieLens dataset show that both the average clustering coefficient and the standard deviation of clustering coefficient decrease with the user reputation, which are different from the results that the average clustering coefficient and the standard deviation of clustering coefficient remain stable regardless of user reputation in the null model, suggesting that the user interest tends to be multiple and the diversity of the user interests is centralized for users with high reputation. Furthermore, we divide users into seven groups according to the user degree and investigate the heterogeneity of rating behavior patterns. The results show that the relation of user reputation and clustering coefficient is obvious for small degree users and weak for large degree users, reflecting an important connection between user degree and collective rating behavior patterns. This work provides a further understanding on the intrinsic association between user collective behaviors and user reputation.
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Oliveira, R. I., R. Ribeiro, and R. Sanchis. "Disparity of clustering coefficients in the Holme‒Kim network model." Advances in Applied Probability 50, no. 3 (September 2018): 918–43. http://dx.doi.org/10.1017/apr.2018.41.
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Abstract The Holme‒Kim random graph process is a variant of the Barabási‒Álbert scale-free graph that was designed to exhibit clustering. In this paper we show that whether the model does indeed exhibit clustering depends on how we define the clustering coefficient. In fact, we find that the local clustering coefficient typically remains positive whereas global clustering tends to 0 at a slow rate. These and other results are proven via martingale techniques, such as Freedman's concentration inequality combined with a bootstrapping argument.
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Yang, Chun-Xia, Min-Xuan Tang, Hai-Qiang Tang, and Qiang-Qiang Deng. "Local-world and cluster-growing weighted networks with controllable clustering." International Journal of Modern Physics C 25, no. 05 (March 11, 2014): 1440009. http://dx.doi.org/10.1142/s0129183114400099.
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We constructed an improved weighted network model by introducing local-world selection mechanism and triangle coupling mechanism based on the traditional BBV model. The model gives power-law distributions of degree, strength and edge weight and presents the linear relationship both between the degree and strength and between the degree and the clustering coefficient. Particularly, the model is equipped with an ability to accelerate the speed increase of strength exceeding that of degree. Besides, the model is more sound and efficient in tuning clustering coefficient than the original BBV model. Finally, based on our improved model, we analyze the virus spread process and find that reducing the size of local-world has a great inhibited effect on virus spread.
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Wang, Yu, Eshwar Ghumare, Rik Vandenberghe, and Patrick Dupont. "Comparison of Different Generalizations of Clustering Coefficient and Local Efficiency for Weighted Undirected Graphs." Neural Computation 29, no. 2 (February 2017): 313–31. http://dx.doi.org/10.1162/neco_a_00914.
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Binary undirected graphs are well established, but when these graphs are constructed, often a threshold is applied to a parameter describing the connection between two nodes. Therefore, the use of weighted graphs is more appropriate. In this work, we focus on weighted undirected graphs. This implies that we have to incorporate edge weights in the graph measures, which require generalizations of common graph metrics. After reviewing existing generalizations of the clustering coefficient and the local efficiency, we proposed new generalizations for these graph measures. To be able to compare different generalizations, a number of essential and useful properties were defined that ideally should be satisfied. We applied the generalizations to two real-world networks of different sizes. As a result, we found that not all existing generalizations satisfy all essential properties. Furthermore, we determined the best generalization for the clustering coefficient and local efficiency based on their properties and the performance when applied to two networks. We found that the best generalization of the clustering coefficient is [Formula: see text], defined in Miyajima and Sakuragawa ( 2014 ), while the best generalization of the local efficiency is [Formula: see text], proposed in this letter. Depending on the application and the relative importance of sensitivity and robustness to noise, other generalizations may be selected on the basis of the properties investigated in this letter.
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Liu, Saisai, and Zhengyou Xia. "A two-stage BFS local community detection algorithm based on node transfer similarity and Local Clustering Coefficient." Physica A: Statistical Mechanics and its Applications 537 (January 2020): 122717. http://dx.doi.org/10.1016/j.physa.2019.122717.
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GRABOWSKI, ANDRZEJ, and ROBERT A. KOSIŃSKI. "PROPERTIES OF AN EVOLVING DIRECTED NETWORK WITH LOCAL RULES AND INTRINSIC VARIABLES." International Journal of Modern Physics C 18, no. 01 (January 2007): 43–52. http://dx.doi.org/10.1142/s0129183107010243.
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We present a simple model of an evolving directed network based on local rules. It leads to a complex network with the properties of real systems, like scale-free distribution of outgoing and incoming connectivity, and a hierarchical structure. Each node is characterised by an intrinsic variable S, and the number of outgoing links k out . As a result of network evolution the number of nodes and links (as well as their location) changes in time. For critical values of control parameters there is a transition to a scale-free network. Results for connectivity distribution found analytically agree with numerical calculations. Our model also reproduces other nontrivial properties of real networks, e.g. a large clustering coefficient and weak correlations between the age of a node and its connectivity. We have discovered an unexpected phenomenon that noise can increase the value of the clustering coefficient, whose large value is characteristic for a regular network.
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Li, Xin Ye. "XML Document Clustering Based on Spectral Analysis Method." Advanced Materials Research 219-220 (March 2011): 304–7. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.304.
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While K-Means algorithm usually gets local optimal solution, spectral clustering method can obtain satisfying clustering results through embedding the data points into a new space in which clusters are tighter. Since traditional spectral clustering method uses Gauss Kernel Function to compute the similarity between two points, the selection of scale parameter σ is related with domain knowledge usually. This paper uses spectral method to cluster XML documents. To consider both element and structure of XML documents, this paper proposes to use path feature to represent XML document; to avoild the selection of scale parameter σ, it also proposes to use Jaccard coefficient to compute the similarity between two XML documents. Experiment shows that using Jaccard coefficient to compute the similarity is effective, the clustering result is correct.
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Tsonis, Anastasios A., Kyle L. Swanson, and Geli Wang. "Estimating the clustering coefficient in scale-free networks on lattices with local spatial correlation structure." Physica A: Statistical Mechanics and its Applications 387, no. 21 (September 2008): 5287–94. http://dx.doi.org/10.1016/j.physa.2008.05.048.
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Pan, Xiaohui, Guiqiong Xu, Bing Wang, and Tao Zhang. "A Novel Community Detection Algorithm Based on Local Similarity of Clustering Coefficient in Social Networks." IEEE Access 7 (2019): 121586–98. http://dx.doi.org/10.1109/access.2019.2937580.
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H.L, Aravinda, and M. V. Sudhamani. "Liver tumour classification using average correction higher order local autocorrelation coefficient and legendre moments." International Journal of Engineering & Technology 7, no. 2.6 (March 11, 2018): 306. http://dx.doi.org/10.14419/ijet.v7i2.6.11269.
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The major reasons for liver carcinoma are cirrhosis and hepatitis. In order to identify carcinoma in the liver abdominal CT images are used. From abdominal CT images, segmentation of liver portion using adaptive region growing, tumor segmentation from extracted liver using Simple Linear Iterative Clustering is already implemented. In this paper, classification of tumors as benign or malignant is accomplished using Rough-set classifier based on texture feature extracted using Average Correction Higher Order Local Autocorrelation Coefficients and Legendre moments. Classification accuracy achieved in proposed scheme is 90%. The results obtained are promising and have been compared with existing methods.
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Yang, Xu-Hua, Hai-Feng Zhang, Fei Ling, Zhi Cheng, Guo-Qing Weng, and Yu-Jiao Huang. "Link prediction based on local community properties." International Journal of Modern Physics B 30, no. 31 (December 5, 2016): 1650222. http://dx.doi.org/10.1142/s0217979216502222.
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The link prediction algorithm is one of the key technologies to reveal the inherent rule of network evolution. This paper proposes a novel link prediction algorithm based on the properties of the local community, which is composed of the common neighbor nodes of any two nodes in the network and the links between these nodes. By referring to the node degree and the condition of assortativity or disassortativity in a network, we comprehensively consider the effect of the shortest path and edge clustering coefficient within the local community on node similarity. We numerically show the proposed method provide good link prediction results.
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Chen, Bing, Dong Dong Yang, Gang Lu, and Hong Xiao Feng. "Two-Phase Image Segmentation with Nonlocal Mean Filter and Kernel Evolutionary Clustering in Local Learning." Applied Mechanics and Materials 536-537 (April 2014): 172–75. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.172.
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In this study, a novel two-phase image segmentation algorithm (TPIS) by using nonlocal mean filter and kernel evolutionary clustering in local learning is proposed. Currently, the difficulties for image segmentation lie in its vast pixels with overlapping characteristic and the noise in the different process of imaging. Here, we want to use nonlocal mean filter to remove different types of noise in the image, and then, two kernel clustering indices are designed in evolutionary optimization. Besides, the local learning strategy is designed using local coefficient of variation of each local pixels or image patch is employed to update the quality of the local segments. The new algorithm is used to solve different image segmentation tasks. The experimental results show that TPIS is competent for segmenting majority of the test images with high quality.
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Zhou, Wen, and Shuaiqin Zhao. "Community detection based on gravitational coefficient in collaboration network." Modern Physics Letters B 34, no. 14 (February 28, 2020): 2050143. http://dx.doi.org/10.1142/s0217984920501432.
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One important characteristic of complex networks is community structure. How to effectively divide the potential community structure of complex networks has been the focus of scholars because communities may have very different properties than the network. A community is usually defined as a collection of nodes with similar attributes. Generally, nodes in the same community are relatively densely connected to each other, compared with nodes from different communities. From the perspective of clustering, nodes in the same community can be considered as having higher similarities. Therefore, using graph clustering algorithms for community detection is theoretically feasible. Collaborative networks are special complex networks. A collaborative relationship tends to connect to multiple collaborators, which makes it hard to build collaborative networks by abstracting the collaboration into edges. Based on characteristics of the collaborative network, we expand the cluster similarity index and propose a gravitational coefficient index to measure the similarity of nodes and subsequently design community detection algorithms. Experiments using real datasets show that the proposed algorithm can obtain higher quality community partitioning results and avoid falling into local optimal solutions to obtain larger-scale communities than classical community detection algorithms.
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Perdhana, Fyannita, Tri Hastini, and Iskandar Ishaq. "West Java local rice panicle branching architecture." E3S Web of Conferences 306 (2021): 01013. http://dx.doi.org/10.1051/e3sconf/202130601013.
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As local varieties of rice have a very important role as a source of valuable traits in developing high yielding variety through plant breeding programs, it is needed to be characterized. Panicle branching characterization is one of the efforts to understand local varieties of rice characteristics more. We have observed thirtheen characters of panicle branching on 24 West Java local rice varieties. Five panicles of each varieties as accession was observed and statistical analysed. Tukey’s Honest Significant Difference (HSD) test showed differences among accessions in all panicle branching characteristics observed. Based on Principles Component Analysis (PCA), the panicle branching characters observed generally showed the same direction, but among them were not always to be correlated. In the result of clustering based on the ward linkage method, the accessions were divided into two clusters. The first one had 8 members, and the second one had 16 members. The cophenetic correlation coefficient was 0.60, indicated that the clustering through standardized value was faithfully enough to represent the original distances. The result of this research can provide the information for breeder in selecting rice genotypes which have more seeds per panicle as parent in assembling new high yielding rice varieties.
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GÓMEZ-GARDEÑES, JESÚS, and YAMIR MORENO. "SYNCHRONIZATION OF NETWORKS WITH VARIABLE LOCAL PROPERTIES." International Journal of Bifurcation and Chaos 17, no. 07 (July 2007): 2501–7. http://dx.doi.org/10.1142/s0218127407018579.
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We study the synchronization transition of Kuramoto oscillators in scale-free networks that are characterized by tunable local properties. Specifically, we perform a detailed finite size scaling analysis and inspect how the critical properties of the dynamics change when the clustering coefficient and the average shortest path length are varied. The results show that the onset of synchronization does depend on these properties, though the dependence is smooth. On the contrary, the appearance of complete synchronization is radically affected by the structure of the networks. Our study highlights the need of exploring the whole phase diagram and not only the stability of the fully synchronized state, where most studies have been done up to now.
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FRAHLING, GEREON, and CHRISTIAN SOHLER. "A FAST k-MEANS IMPLEMENTATION USING CORESETS." International Journal of Computational Geometry & Applications 18, no. 06 (December 2008): 605–25. http://dx.doi.org/10.1142/s0218195908002787.
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In this paper we develop an efficient implementation for a k-means clustering algorithm. The algorithm is based on a combination of Lloyd's algorithm with random swapping of centers to avoid local minima. This approach was proposed by Mount 30. The novel feature of our algorithms is the use of coresets to speed up the algorithm. A coreset is a small weighted set of points that approximates the original point set with respect to the considered problem. We use a coreset construction described in 12. Our algorithm first computes a solution on a very small coreset. Then in each iteration the previous solution is used as a starting solution on a refined, i.e. larger, coreset. To evaluate the performance of our algorithm we compare it with algorithm KMHybrid 30 on typical 3D data sets for an image compression application and on artificially created instances. Our data sets consist of 300,000 to 4.9 million points. Our algorithm outperforms KMHybrid on most of these input instances. Additionally, the quality of the solutions computed by our algorithm deviates significantly less than that of KMHybrid. We conclude that the use of coresets has two effects. First, it can speed up algorithms significantly. Secondly, in variants of Lloyd's algorithm, it reduces the dependency on the starting solution and thus makes the algorithm more stable. Finally, we propose the use of coresets as a heuristic to approximate the average silhouette coefficient of clusterings. The average silhouette coefficient is a measure for the quality of a clustering that is independent of the number of clusters k. Hence, it can be used to compare the quality of clusterings for different sizes of k. To show the applicability of our approach we computed clusterings and approximate average silhouette coefficient for k = 1,…, 100 for our input instances and discuss the performance of our algorithm in detail.
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Li, Jiada, Daniyal Hassan, Simon Brewer, and Robert Sitzenfrei. "Is Clustering Time-Series Water Depth Useful? An Exploratory Study for Flooding Detection in Urban Drainage Systems." Water 12, no. 9 (August 30, 2020): 2433. http://dx.doi.org/10.3390/w12092433.
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As sensor measurements emerge in urban water systems, data-driven unsupervised machine learning algorithms have drawn tremendous interest in event detection and hydraulic water level and flow prediction recently. However, most of them are applied in water distribution systems and few studies consider using unsupervised cluster analysis to group the time-series hydraulic-hydrologic data in stormwater urban drainage systems. To improve the understanding of how cluster analysis contributes to flooding location detection, this study compared the performance of K-means clustering, agglomerative clustering, and spectral clustering in uncovering time-series water depth dissimilarity. In this work, the water depth datasets are simulated by an urban drainage model and then formatted for a clustering problem. Three standard performance evaluation metrics, namely the silhouette coefficient index, Calinski–Harabasz index, and Davies–Bouldin index are employed to assess the clustering performance in flooding detection under various storms. The results show that silhouette coefficient index and Davies–Bouldin index are more suitable for assessing the performance of K-means and agglomerative clustering, while the Calinski–Harabasz index only works for spectral clustering, indicating these clustering algorithms are metric-dependent flooding indicators. The results also reveal that the agglomerative clustering performs better in detecting short-duration events while K-means and spectral clustering behave better in detecting long-duration floods. The findings of these investigations can be employed in urban stormwater flood detection at the specific junction-level sites by using the occurrence of anomalous changes in water level of correlated clusters as flood early warning for the local neighborhoods.
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WANG, BING, HUANWEN TANG, ZHONGZHI ZHANG, and ZHILONG XIU. "EVOLVING SCALE-FREE NETWORK MODEL WITH TUNABLE CLUSTERING." International Journal of Modern Physics B 19, no. 26 (October 20, 2005): 3951–59. http://dx.doi.org/10.1142/s0217979205032437.
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The Barabási–Albert (BA) model is extended to include the concept of local world and the microscopic event of adding edges. With probability p, we add a new node with m edges which preferentially link to the nodes presented in the network; with probability 1-p, we add m edges among the present nodes. A node is preferentially selected by its degree to add an edge randomly among its neighbors. Using the continuum theory and the rate equation method we get the analytical expressions of the power-law degree distribution with exponent γ=3 and the clustering coefficient c(k)~k-1+c. The analytical expressions are in good agreement with the numerical calculations.
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Kong, Weizheng, Yaohua Wang, Hongcai Dai, Liujun Zhao, and Chunming Wang. "Analysis of energy consumption structure based on K-means clustering algorithm." E3S Web of Conferences 267 (2021): 01054. http://dx.doi.org/10.1051/e3sconf/202126701054.
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In order to solve the problem of huge and messy data in the process of analyzing energy consumption structure in different regions, an energy consumption structure analysis method based on K-means clustering algorithm is proposed, and the elbow method and contour coefficient method are used to analyze the data in Qinghai Province. The consumption structure was analyzed and the algorithm was verified. The results show that the algorithm can efficiently and quickly perform data mining and clustering based on local economic and environmental characteristics, which greatly improve the convenience of energy consumption structure analysis.
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Liang, Zhenhu, Lei Cheng, Shuai Shao, Xing Jin, Tao Yu, Jamie W. Sleigh, and Xiaoli Li. "Information Integration and Mesoscopic Cortical Connectivity during Propofol Anesthesia." Anesthesiology 132, no. 3 (March 1, 2020): 504–24. http://dx.doi.org/10.1097/aln.0000000000003015.
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Abstract Background The neurophysiologic mechanisms of propofol-induced loss of consciousness have been studied in detail at the macro (scalp electroencephalogram) and micro (spiking or local field potential) scales. However, the changes in information integration and cortical connectivity during propofol anesthesia at the mesoscopic level (the cortical scale) are less clear. Methods The authors analyzed electrocorticogram data recorded from surgical patients during propofol-induced unconsciousness (n = 9). A new information measure, genuine permutation cross mutual information, was used to analyze how electrocorticogram cross-electrode coupling changed with electrode-distances in different brain areas (within the frontal, parietal, and temporal regions, as well as between the temporal and parietal regions). The changes in cortical networks during anesthesia—at nodal and global levels—were investigated using clustering coefficient, path length, and nodal efficiency measures. Results In all cortical regions, and in both wakeful and unconscious states (early and late), the genuine permutation cross mutual information and the percentage of genuine connections decreased with increasing distance, especially up to about 3 cm. The nodal cortical network metrics (the nodal clustering coefficients and nodal efficiency) decreased from wakefulness to unconscious state in the cortical regions we analyzed. In contrast, the global cortical network metrics slightly increased in the early unconscious state (the time span from loss of consciousness to 200 s after loss of consciousness), as compared with wakefulness (normalized average clustering coefficient: 1.05 ± 0.01 vs. 1.06 ± 0.03, P = 0.037; normalized average path length: 1.02 ± 0.01 vs. 1.04 ± 0.01, P = 0.021). Conclusions The genuine permutation cross mutual information reflected propofol-induced coupling changes measured at a cortical scale. Loss of consciousness was associated with a redistribution of the pattern of information integration; losing efficient global information transmission capacity but increasing local functional segregation in the cortical network. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New
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Masoud, Mohammad Z., Yousef Jaradat, Ismael Jannoud, and Mustafa A. Al Sibahee. "A hybrid clustering routing protocol based on machine learning and graph theory for energy conservation and hole detection in wireless sensor network." International Journal of Distributed Sensor Networks 15, no. 6 (June 2019): 155014771985823. http://dx.doi.org/10.1177/1550147719858231.
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In this work, a new hybrid clustering routing protocol is proposed to prolong network life time through detecting holes and edges nodes. The detection process attempts to generate a connected graph without any isolated nodes or clusters that have no connection with the sink node. To this end, soft clustering/estimation maximization with graph metrics, PageRank, node degree, and local cluster coefficient, has been utilized. Holes and edges detection process is performed by the sink node to reduce energy consumption of wireless sensor network nodes. The clustering process is dynamic among sensor nodes. Hybrid clustering routing protocol–hole detection converts the network into a number of rings to overcome transmission distances. We compared hybrid clustering routing protocol–hole detection with four different protocols. The accuracy of detection reached 98%. Moreover, network life time has prolonged 10%. Finally, hybrid clustering routing protocol–hole detection has eliminated the disconnectivity in the network for more than 80% of network life time.
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Kaczmarek, Mirosława. "Spatial diversity of poverty symptoms in Poland." Wiadomości Statystyczne. The Polish Statistician 61, no. 8 (August 29, 2016): 18–31. http://dx.doi.org/10.5604/01.3001.0014.1061.
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The article presents the results of the clustering voivodships based on variables characterizing the phenomenon of poverty. The analysis is based on data available in the CSO’s Local Data Bank. The selection of diagnostic features was made on the basis of the coefficients of variation and Pearson’s r correlation coefficient. The grouping of voivodships was made using the k-means method. There were created four categories of voivodships differing in the symptoms of poverty. In order to answer the question whether the changes in the symptoms of poverty are taking place on the map of Poland, an analysis was conducted in two periods: for 2013 and 2008.
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Zheng, Yi, Fang Liu, and Yong-Wang Gong. "Robustness in Weighted Networks with Cluster Structure." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/292465.
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The vulnerability of complex systems induced by cascade failures revealed the comprehensive interaction of dynamics with network structure. The effect on cascade failures induced by cluster structure was investigated on three networks, small-world, scale-free, and module networks, of which the clustering coefficient is controllable by the random walk method. After analyzing the shifting process of load, we found that the betweenness centrality and the cluster structure play an important role in cascading model. Focusing on this point, properties of cascading failures were studied on model networks with adjustable clustering coefficient and fixed degree distribution. In the proposed weighting strategy, the path length of an edge is designed as the product of the clustering coefficient of its end nodes, and then the modified betweenness centrality of the edge is calculated and applied in cascade model as its weights. The optimal region of the weighting scheme and the size of the survival components were investigated by simulating the edge removing attack, under the rule of local redistribution based on edge weights. We found that the weighting scheme based on the modified betweenness centrality makes all three networks have better robustness against edge attack than the one based on the original betweenness centrality.
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Wang, Lifang, Chaoyu Shi, Suzhen Lin, Pinle Qin, and Yanli Wang. "Convolutional Sparse Representation and Local Density Peak Clustering for Medical Image Fusion." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 07 (October 22, 2019): 2057003. http://dx.doi.org/10.1142/s0218001420570037.
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Aiming at the problem of insufficient detail retention in multimodal medical image fusion (MMIF) based on sparse representation (SR), an MMIF method based on density peak clustering and convolution sparse representation (CSR-DPC) is proposed. First, the base layer is obtained based on the registered input image by the averaging filter, and the original image minus the base layer to obtain the detail layer. Second, for retaining the details of the fused image, the detail layer image is fused by CSR to obtain the fused detail layer image, then the base layer image is segmented into several image blocks, and the blocks are clustered by using DPC to obtain some clusters, and each class cluster is trained to obtain a sub-dictionary, and all the sub-dictionaries are fused to obtain an adaptive dictionary. The sparse coefficient is fused through the learned adaptive dictionary, and the fused base layer image is obtained through reconstruction. Finally, fusing the detail layer and the base layer and reconstructing them forms the ultimate fused image. Experiments show that compared to the state-of-the-art two multi-scale transformation methods and five SR methods, the proposed method(CSR-DPC) outperforms the other methods in terms of the image details, the visual quality and the objective evaluation index, which can be helpful for clinical diagnosis and adjuvant treatment.
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Sivakumar, B., and F. M. Woldemeskel. "Complex networks for streamflow dynamics." Hydrology and Earth System Sciences 18, no. 11 (November 20, 2014): 4565–78. http://dx.doi.org/10.5194/hess-18-4565-2014.
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Abstract. Streamflow modeling is an enormously challenging problem, due to the complex and nonlinear interactions between climate inputs and landscape characteristics over a wide range of spatial and temporal scales. A basic idea in streamflow studies is to establish connections that generally exist, but attempts to identify such connections are largely dictated by the problem at hand and the system components in place. While numerous approaches have been proposed in the literature, our understanding of these connections remains far from adequate. The present study introduces the theory of networks, in particular complex networks, to examine the connections in streamflow dynamics, with a particular focus on spatial connections. Monthly streamflow data observed over a period of 52 years from a large network of 639 monitoring stations in the contiguous US are studied. The connections in this streamflow network are examined primarily using the concept of clustering coefficient, which is a measure of local density and quantifies the network's tendency to cluster. The clustering coefficient analysis is performed with several different threshold levels, which are based on correlations in streamflow data between the stations. The clustering coefficient values of the 639 stations are used to obtain important information about the connections in the network and their extent, similarity, and differences between stations/regions, and the influence of thresholds. The relationship of the clustering coefficient with the number of links/actual links in the network and the number of neighbors is also addressed. The results clearly indicate the usefulness of the network-based approach for examining connections in streamflow, with important implications for interpolation and extrapolation, classification of catchments, and predictions in ungaged basins.
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Sivakumar, B., and F. M. Woldemeskel. "Complex networks for streamflow dynamics." Hydrology and Earth System Sciences Discussions 11, no. 7 (July 2, 2014): 7255–89. http://dx.doi.org/10.5194/hessd-11-7255-2014.
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Abstract. Streamflow modeling is an enormously challenging problem, due to the complex and nonlinear interactions between climate inputs and landscape characteristics over a wide range of spatial and temporal scales. A basic idea in streamflow studies is to establish connections that generally exist, but attempts to identify such connections are largely dictated by the problem at hand and the system components in place. While numerous approaches have been proposed in the literature, our understanding of these connections remains far from adequate. The present study introduces the theory of networks, and in particular complex networks, to examine the connections in streamflow dynamics, with a particular focus on spatial connections. Monthly streamflow data observed over a period of 52 years from a large network of 639 monitoring stations in the contiguous United States are studied. The connections in this streamflow network are examined using the concept of clustering coefficient, which is a measure of local density and quantifies the network's tendency to cluster. The clustering coefficient analysis is performed with several different threshold levels, which are based on correlations in streamflow data between the stations. The clustering coefficient values of the 639 stations are used to obtain important information about the connections in the network and their extent, similarity and differences between stations/regions, and the influence of thresholds. The relationship of the clustering coefficient with the number of links/actual links in the network and the number of neighbors is also addressed. The results clearly indicate the usefulness of the network-based approach for examining connections in streamflow, with important implications for interpolation and extrapolation, classification of catchments, and predictions in ungaged basins.
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Li, Mengtian, Ruisheng Zhang, Rongjing Hu, Fan Yang, Yabing Yao, and Yongna Yuan. "Identifying and ranking influential spreaders in complex networks by combining a local-degree sum and the clustering coefficient." International Journal of Modern Physics B 32, no. 06 (February 26, 2018): 1850118. http://dx.doi.org/10.1142/s0217979218501187.
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Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible–infectious–recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.
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Riyadi, Damar, and Aina Musdholifah. "Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based Reasoning." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 12, no. 2 (July 31, 2018): 139. http://dx.doi.org/10.22146/ijccs.30423.
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This study aims to improve the performance of Case-Based Reasoning by utilizing cluster analysis which is used as an indexing method to speed up case retrieval in CBR. The clustering method uses Local Triangular Kernel-based Clustering (LTKC). The cosine coefficient method is used for finding the relevant cluster while similarity value is calculated using Manhattan distance, Euclidean distance, and Minkowski distance. Results of those methods will be compared to find which method gives the best result. This study uses three test data: malnutrition disease, heart disease, and thyroid disease. Test results showed that CBR with LTKC-indexing has better accuracy and processing time than CBR without indexing. The best accuracy on threshold 0.9 of malnutrition disease, obtained using the Euclidean distance which produces 100% accuracy and 0.0722 seconds average retrieval time. The best accuracy on threshold 0.9 of heart disease, obtained using the Minkowski distance which produces 95% accuracy and 0.1785 seconds average retrieval time. The best accuracy on threshold 0.9 of thyroid disease, obtained using the Minkowski distance which produces 92.52% accuracy and 0.3045 average retrieval time. The accuracy comparison of CBR with SOM-indexing, DBSCAN-indexing, and LTKC-indexing for malnutrition diseases and heart disease resulted that they have almost equal accuracy.
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Fleischer, Vinzenz, Nabin Koirala, Amgad Droby, René-Maxime Gracien, Ralf Deichmann, Ulf Ziemann, Sven G. Meuth, Muthuraman Muthuraman, Frauke Zipp, and Sergiu Groppa. "Longitudinal cortical network reorganization in early relapsing–remitting multiple sclerosis." Therapeutic Advances in Neurological Disorders 12 (January 2019): 175628641983867. http://dx.doi.org/10.1177/1756286419838673.
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Background: Network science provides powerful access to essential organizational principles of the brain. The aim of this study was to investigate longitudinal evolution of gray matter networks in early relapsing–remitting MS (RRMS) compared with healthy controls (HCs) and contrast network dynamics with conventional atrophy measurements. Methods: For our longitudinal study, we investigated structural cortical networks over 1 year derived from 3T MRI in 203 individuals (92 early RRMS patients with mean disease duration of 12.1 ± 14.5 months and 101 HCs). Brain networks were computed based on cortical thickness inter-regional correlations and fed into graph theoretical analysis. Network connectivity measures (modularity, clustering coefficient, local efficiency, and transitivity) were compared between patients and HCs, and between patients with and without disease activity. Moreover, we calculated longitudinal brain volume changes and cortical atrophy patterns. Results: Our analyses revealed strengthening of local network properties shown by increased modularity, clustering coefficient, local efficiency, and transitivity over time. These network dynamics were not detectable in the cortex of HCs over the same period and occurred independently of patients’ disease activity. Most notably, the described network reorganization was evident beyond detectable atrophy as characterized by conventional morphometric methods. Conclusion: In conclusion, our findings provide evidence for gray matter network reorganization subsequent to clinical disease manifestation in patients with early RRMS. An adaptive cortical response with increased local network characteristics favoring network segregation could play a primordial role for maintaining brain function in response to neuroinflammation.
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Sunarmodo, Wismu, and Anis Kamilah Hayati. "CLOUD IDENTIFICATION FROM MULTITEMPORAL LANDSAT-8 USING K-MEANS CLUSTERING." International Journal of Remote Sensing and Earth Sciences (IJReSES) 16, no. 2 (April 30, 2020): 157. http://dx.doi.org/10.30536/j.ijreses.2019.v16.a3285.
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In the processing and analysis of remote-sensing data, cloud that interferes with earth-surface data is still a challenge. Many methods have already been developed to identify cloud, and these can be classified into two categories: single-date and multi-date identification. Most of these methods also utilize the thresholding method which itself can be divided into two categories: local thresholding and global thresholding. Local thresholding works locally and is different for each pixel, while global thresholding works similarly for every pixel. To determine the global threshold, two approaches are commonly used: fixed value as threshold and adapted threshold. In this paper, we propose a cloud-identification method with an adapted threshold using K-means clustering. Each related multitemporal pixel is processed using K-means clustering to find the threshold. The threshold is then used to distinguish clouds from non-clouds. By using the L8 Biome cloud-cover assessment as a reference, the proposed method results in Kappa coefficient of above 0.9. Furthermore, the proposed method has lower levels of false negatives and omission errors than the FMask method.
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Zhang, Peng, and Kun She. "A Novel Hierarchical Clustering Approach Based on Universal Gravitation." Mathematical Problems in Engineering 2020 (February 1, 2020): 1–15. http://dx.doi.org/10.1155/2020/6748056.
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The target of the clustering analysis is to group a set of data points into several clusters based on the similarity or distance. The similarity or distance is usually a scalar used in numerous traditional clustering algorithms. Nevertheless, a vector, such as data gravitational force, contains more information than a scalar and can be applied in clustering analysis to promote clustering performance. Therefore, this paper proposes a three-stage hierarchical clustering approach called GHC, which takes advantage of the vector characteristic of data gravitational force inspired by the law of universal gravitation. In the first stage, a sparse gravitational graph is constructed based on the top k data gravitations between each data point and its neighbors in the local region. Then the sparse graph is partitioned into many subgraphs by the gravitational influence coefficient. In the last stage, the satisfactory clustering result is obtained by merging these subgraphs iteratively by using a new linkage criterion. To demonstrate the performance of GHC algorithm, the experiments on synthetic and real-world data sets are conducted, and the results show that the GHC algorithm achieves better performance than the other existing clustering algorithms.
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Yi, Hongtao, Yan Yang, and Chao Zhou. "The Impact of Collaboration Network on Water Resource Governance Performance: Evidence from China’s Yangtze River Delta Region." International Journal of Environmental Research and Public Health 18, no. 5 (March 4, 2021): 2557. http://dx.doi.org/10.3390/ijerph18052557.
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Existing studies rarely examine the relationship between network structure and network performance. To fill this research gap, this article collects inter-local collaboration network data from 41 cities in the Yangtze River Delta region of China from 2009 to 2015. Based on the institutional collective action framework and social capital theory, we propose bridging and bonding hypotheses regarding the impact of network structures on governance performance. We employ social network analysis and panel data regression models to test the hypotheses. The results show that the coefficients for closeness centrality and clustering coefficient are statistically significant in this analysis, Wuxi played a central role in the collaboration network and the region had formed a close partner network, confirming the positive effect of bridging and bonding network social capital structures on network performance.
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Ai, Dongmei, Hongfei Pan, Xiaoxin Li, Min Wu, and Li C. Xia. "Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls." PeerJ 7 (July 29, 2019): e7315. http://dx.doi.org/10.7717/peerj.7315.
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The human gut microbiota plays a major role in maintaining human health and was recently recognized as a promising target for disease prevention and treatment. Many diseases are traceable to microbiota dysbiosis, implicating altered gut microbial ecosystems, or, in many cases, disrupted microbial enzymes carrying out essential physio-biochemical reactions. Thus, the changes of essential microbial enzyme levels may predict human disorders. With the rapid development of high-throughput sequencing technologies, metagenomics analysis has emerged as an important method to explore the microbial communities in the human body, as well as their functionalities. In this study, we analyzed 156 gut metagenomics samples from patients with colorectal cancer (CRC) and adenoma, as well as that from healthy controls. We estimated the abundance of microbial enzymes using the HMP Unified Metabolic Analysis Network method and identified the differentially abundant enzymes between CRCs and controls. We constructed enzymatic association networks using the extended local similarity analysis algorithm. We identified CRC-associated enzymic changes by analyzing the topological features of the enzymatic association networks, including the clustering coefficient, the betweenness centrality, and the closeness centrality of network nodes. The network topology of enzymatic association network exhibited a difference between the healthy and the CRC environments. The ABC (ATP binding cassette) transporter and small subunit ribosomal protein S19 enzymes, had the highest clustering coefficient in the healthy enzymatic networks. In contrast, the Adenosylhomocysteinase enzyme had the highest clustering coefficient in the CRC enzymatic networks. These enzymic and metabolic differences may serve as risk predictors for CRCs and are worthy of further research.
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LI, PING, QINGZHEN ZHAO, and HAITANG WANG. "A WEIGHTED LOCAL-WORLD EVOLVING NETWORK MODEL BASED ON THE EDGE WEIGHTS PREFERENTIAL SELECTION." International Journal of Modern Physics B 27, no. 12 (April 29, 2013): 1350039. http://dx.doi.org/10.1142/s0217979213500392.
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In this paper, we use the edge weights preferential attachment mechanism to build a new local-world evolutionary model for weighted networks. It is different from previous papers that the local-world of our model consists of edges instead of nodes. Each time step, we connect a new node to two existing nodes in the local-world through the edge weights preferential selection. Theoretical analysis and numerical simulations show that the scale of the local-world affect on the weight distribution, the strength distribution and the degree distribution. We give the simulations about the clustering coefficient and the dynamics of infectious diseases spreading. The weight dynamics of our network model can portray the structure of realistic networks such as neural network of the nematode C. elegans and Online Social Network.
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Tooley, Ursula A., Allyson P. Mackey, Rastko Ciric, Kosha Ruparel, Tyler M. Moore, Ruben C. Gur, Raquel E. Gur, Theodore D. Satterthwaite, and Danielle S. Bassett. "Associations between Neighborhood SES and Functional Brain Network Development." Cerebral Cortex 30, no. 1 (April 11, 2019): 1–19. http://dx.doi.org/10.1093/cercor/bhz066.
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Abstract Higher socioeconomic status (SES) in childhood is associated with stronger cognitive abilities, higher academic achievement, and lower incidence of mental illness later in development. While prior work has mapped the associations between neighborhood SES and brain structure, little is known about the relationship between SES and intrinsic neural dynamics. Here, we capitalize upon a large cross-sectional community-based sample (Philadelphia Neurodevelopmental Cohort, ages 8–22 years, n = 1012) to examine associations between age, SES, and functional brain network topology. We characterize this topology using a local measure of network segregation known as the clustering coefficient and find that it accounts for a greater degree of SES-associated variance than mesoscale segregation captured by modularity. High-SES youth displayed stronger positive associations between age and clustering than low-SES youth, and this effect was most pronounced for regions in the limbic, somatomotor, and ventral attention systems. The moderating effect of SES on positive associations between age and clustering was strongest for connections of intermediate length and was consistent with a stronger negative relationship between age and local connectivity in these regions in low-SES youth. Our findings suggest that, in late childhood and adolescence, neighborhood SES is associated with variation in the development of functional network structure in the human brain.
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WANG, JING, and LILI RONG. "SIMILARITY INDEX BASED ON THE INFORMATION OF NEIGHBOR NODES FOR LINK PREDICTION OF COMPLEX NETWORK." Modern Physics Letters B 27, no. 06 (February 6, 2013): 1350039. http://dx.doi.org/10.1142/s0217984913500395.
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Link prediction in complex networks has attracted much attention recently. Many local similarity measures based on the measurements of node similarity have been proposed. Among these local similarity indices, the neighborhood-based indices Common Neighbors (CN), Adamic-Adar (AA) and Resource Allocation (RA) index perform best. It is found that the node similarity indices required only information on the nearest neighbors are assigned high scores and have very low computational complexity. In this paper, a new index based on the contribution of common neighbor nodes to edges is proposed and shown to have competitively good or even better prediction than other neighborhood-based indices especially for the network with low clustering coefficient with its high efficiency and simplicity.
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Min, Jae-Sik, Moon-Soo Park, Jung-Hoon Chae, and Minsoo Kang. "Integrated System for Atmospheric Boundary Layer Height Estimation (ISABLE) using a ceilometer and microwave radiometer." Atmospheric Measurement Techniques 13, no. 12 (December 21, 2020): 6965–87. http://dx.doi.org/10.5194/amt-13-6965-2020.
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Abstract. Accurate boundary layer structure and height are critical in the analysis of the features of air pollutants and local circulation. Although surface-based remote sensing instruments provide a high temporal resolution of the boundary layer structure, there are numerous uncertainties in terms of the accurate determination of the atmospheric boundary layer heights (ABLHs). In this study, an algorithm for an integrated system for ABLH estimation (ISABLE) was developed and applied to the vertical profile data obtained using a ceilometer and a microwave radiometer in Seoul city, Korea. A maximum of 19 ABLHs were estimated via the conventional time-variance, gradient, wavelet, and clustering methods using the backscatter coefficient from the ceilometer. Meanwhile, several stable boundary layer heights were extracted through near-surface inversion and environmental lapse rate methods using the potential temperature from the microwave radiometer. The ISABLE algorithm can find an optimal ABLH from post-processing, such as k-means clustering and density-based spatial clustering of applications with noise (DBSCAN) techniques. It was found that the ABLH determined using ISABLE exhibited more significant correlation coefficients and smaller mean bias and root mean square error between the radiosonde-derived ABLHs than those obtained using the most conventional methods. Clear skies exhibited higher daytime ABLH than cloudy skies, and the daily maximum ABLH was recorded in summer because of the more intense radiation. The ABLHs estimated by ISABLE are expected to contribute to the parameterization of vertical diffusion in the atmospheric boundary layer.
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Michielse, Stijn, Iris Lange, Jindra Bakker, Liesbet Goossens, Simone Verhagen, Marieke Wichers, Ritsaert Lieverse, et al. "White matter microstructure and network-connectivity in emerging adults with subclinical psychotic experiences." Brain Imaging and Behavior 14, no. 5 (June 10, 2019): 1876–88. http://dx.doi.org/10.1007/s11682-019-00129-0.
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AbstractGroup comparisons of individuals with psychotic disorder and controls have shown alterations in white matter microstructure. Whether white matter microstructure and network connectivity is altered in adolescents with subclinical psychotic experiences (PE) at the lowest end of the psychosis severity spectrum is less clear. DWI scan were acquired in 48 individuals with PE and 43 healthy controls (HC). Traditional tensor-derived indices: Fractional Anisotropy, Axial Diffusivity, Mean Diffusivity and Radial Diffusivity, as well as network connectivity measures (global/local efficiency and clustering coefficient) were compared between the groups. Subclinical psychopathology was assessed with the Community Assessment of Psychic Experiences (CAPE) and Montgomery–Åsberg Depression Rating Scale (MADRS) questionnaires and, in order to capture momentary subclinical expression of psychosis, the Experience Sampling Method (ESM) questionnaires. Within the PE-group, interactions between subclinical (momentary) symptoms and brain regions in the model of tensor-derived indices and network connectivity measures were investigated in a hypothesis-generating fashion. Whole brain analyses showed no group differences in tensor-derived indices and network connectivity measures. In the PE-group, a higher positive symptom distress score was associated with both higher local efficiency and clustering coefficient in the right middle temporal pole. The findings indicate absence of microstructural white matter differences between emerging adults with subclinical PE and controls. In the PE-group, attenuated symptoms were positively associated with network efficiency/cohesion, which requires replication and may indicate network alterations in emerging mild psychopathology.
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Feklicheva, Inna, Ilya Zakharov, Nadezda Chipeeva, Ekaterina Maslennikova, Svetlana Korobova, Timofey Adamovich, Victoria Ismatullina, and Sergey Malykh. "Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity." Brain Sciences 11, no. 1 (January 13, 2021): 94. http://dx.doi.org/10.3390/brainsci11010094.
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The present study investigates the relationship between individual differences in verbal and non-verbal cognitive abilities and resting-state EEG network characteristics. We used a network neuroscience approach to analyze both large-scale topological characteristics of the whole brain as well as local brain network characteristics. The characteristic path length, modularity, and cluster coefficient for different EEG frequency bands (alpha, high and low; beta1 and beta2, and theta) were calculated to estimate large-scale topological integration and segregation properties of the brain networks. Betweenness centrality, nodal clustering coefficient, and local connectivity strength were calculated as local network characteristics. We showed that global network integration measures in the alpha band were positively correlated with non-verbal intelligence, especially with the more difficult part of the test (Raven’s total scores and E series), and the ability to operate with verbal information (the “Conclusions” verbal subtest). At the same time, individual differences in non-verbal intelligence (Raven’s total score and C series), and vocabulary subtest of the verbal intelligence tests, were negatively correlated with the network segregation measures. Our results show that resting-state EEG functional connectivity can reveal the functional architecture associated with an individual difference in cognitive performance.
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Jasim, Wala’a, and Rana Mohammed. "A Survey on Segmentation Techniques for Image Processing." Iraqi Journal for Electrical and Electronic Engineering 17, no. 2 (August 16, 2021): 73–93. http://dx.doi.org/10.37917/ijeee.17.2.10.
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The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image segmentation are splitting the images into a few parts on the basis of image’s features including texture, color, pixel intensity value and so on. With regard to the presented study, many approaches of image segmentation are reviewed and discussed. The techniques of segmentation might be categorized into six classes: First, thresholding segmentation techniques such as global thresholding (iterative thresholding, minimum error thresholding, otsu's, optimal thresholding, histogram concave analysis and entropy based thresholding), local thresholding (Sauvola’s approach, T.R Singh’s approach, Niblack’s approaches, Bernsen’s approach Bruckstein’s and Yanowitz method and Local Adaptive Automatic Binarization) and dynamic thresholding. Second, edge-based segmentation techniques such as gray-histogram technique, gradient based approach (laplacian of gaussian, differential coefficient approach, canny approach, prewitt approach, Roberts approach and sobel approach). Thirdly, region based segmentation approaches including Region growing techniques (seeded region growing (SRG), statistical region growing, unseeded region growing (UsRG)), also merging and region splitting approaches. Fourthly, clustering approaches, including soft clustering (fuzzy C-means clustering (FCM)) and hard clustering (K-means clustering). Fifth, deep neural network techniques such as convolution neural network, recurrent neural networks (RNNs), encoder-decoder and Auto encoder models and support vector machine. Finally, hybrid techniques such as evolutionary approaches, fuzzy logic and swarm intelligent (PSO and ABC techniques) and discusses the pros and cons of each method.
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Huang, Faming, Jianbo Yang, Biao Zhang, Yijing Li, Jinsong Huang, and Na Chen. "Regional Terrain Complexity Assessment Based on Principal Component Analysis and Geographic Information System: A Case of Jiangxi Province, China." ISPRS International Journal of Geo-Information 9, no. 9 (September 8, 2020): 539. http://dx.doi.org/10.3390/ijgi9090539.
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Regional terrain complexity assessment (TCA) is an important theoretical foundation for geological feature identification, hydrological information extraction and land resources utilization. However, the previous TCA models have many disadvantages; for example, comprehensive consideration and redundancy information analysis of terrain factors is lacking, and the terrain complexity index is difficult to quantify. To overcome these drawbacks, a TCA model based on principal component analysis (PCA) and a geographic information system (GIS) is proposed. Taking Jiangxi province of China as an example, firstly, ten terrain factors are extracted using a digital elevation model (DEM) in GIS software. Secondly, PCA is used to analyze the information redundancy of these terrain factors and deal with data compression. Then, the comprehensive evaluation of the compressed terrain factors is conducted to obtain quantitative terrain complexity indexes and a terrain complexity map (TCM). Finally, the TCM produced by the PCA method is compared with those produced by the slope-only, the variation coefficient and K-means clustering models based on the topographic map drawn by the Bureau of Land and Resources of Jiangxi province. Meanwhile, the TCM is also verified by the actual three-dimensional aerial images. Results show that the correlation coefficients between the TCMs produced by the PCA, slope-only, variable coefficient and K-means clustering models and the local topographic map are 0.894, 0.763, 0.816 and 0.788, respectively. It is concluded that the TCM of the PCA method matches well with the actual field terrain features, and the PCA method can reflect the regional terrain complexity characteristics more comprehensively and accurately when compared to the other three methods.
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Ortiz, Erick, Krunoslav Stingl, Jana Münßinger, Christoph Braun, Hubert Preissl, and Paolo Belardinelli. "Weighted Phase Lag Index and Graph Analysis: Preliminary Investigation of Functional Connectivity during Resting State in Children." Computational and Mathematical Methods in Medicine 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/186353.
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Resting state functional connectivity of MEG data was studied in 29 children (9-10 years old). The weighted phase lag index (WPLI) was employed for estimating connectivity and compared to coherence. To further evaluate the network structure, a graph analysis based on WPLI was used to determine clustering coefficient (C) and betweenness centrality (BC) as local coefficients as well as the characteristic path length (L) as a parameter for global interconnectedness. The network’s modular structure was also calculated to estimate functional segregation. A seed region was identified in the central occipital area based on the power distribution at the sensor level in the alpha band. WPLI reveals a specific connectivity map different from power and coherence. BC and modularity show a strong level of connectedness in the occipital area between lateral and central sensors.Cshows different isolated areas of occipital sensors. Globally, a network with the shortestLis detected in the alpha band, consistently with the local results. Our results are in agreement with findings in adults, indicating a similar functional network in children at this age in the alpha band. The integrated use of WPLI and graph analysis can help to gain a better description of resting state networks.
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Zhang, Zhe, and Jianhua Song. "A Robust Brain MRI Segmentation and Bias Field Correction Method Integrating Local Contextual Information into a Clustering Model." Applied Sciences 9, no. 7 (March 29, 2019): 1332. http://dx.doi.org/10.3390/app9071332.
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The segmentation results of brain magnetic resonance imaging (MRI) have important guiding significance for subsequent clinical diagnosis and treatment. However, brain MRI segmentation is a complex and challenging problem due to the inevitable noise or intensity inhomogeneity. A novel robust clustering with local contextual information (RC_LCI) model was used in this study which accurately segmented brain MRI corrupted by noise and intensity inhomogeneity. For pixels in the neighborhood of the central pixel, a weighting scheme combining local contextual information was used to generate the corresponding anisotropic weight to update the current central pixel and thus remove noisy pixels. Then, a multiplicative framework consisting of the product of a real image and a bias field could effectively segment brain MRI and estimate the bias field. Further, a linear combination of basis functions was introduced to guarantee the bias field properties. Therefore, compared with state-of-the-art models, the segmentation result obtained by RC_LCI was increased by 0.195 0.125 in terms of the Jaccard similarity coefficient. Both visual experimental results and quantitative evaluation demonstrate the superiority of RC_LCI on real and synthetic images.
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Yang, Dongsheng, Ting Li, Bo Hu, Jing Gao, and Chunsheng Wang. "Multimode Process Monitoring Based on Geodesic Distance." International Journal of Software Engineering and Knowledge Engineering 28, no. 09 (September 2018): 1225–48. http://dx.doi.org/10.1142/s0218194018400132.
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A novel monitoring strategy is proposed for multimode process in which mode clustering and fault detection based on geodesic distance (GD) are integrated. To start with, the empowered adjacency matrix of normalized training dataset is obtained and improved Dijkstra algorithm (IDA) is utilized to calculate the geodesic distance between each sample data so as to characterize the shortest distance of the nonlinear data within local areas accurately. Next, GD matrix algorithm is presented as an optimal clustering solution for a multimode process dataset. Then, the GDS model is established in each operating mode. Monitoring statistics based on the power of geodesic distance are structured based on square sum of Euclidean distances. Once the test data is detected as fault data, mode location based on deviation coefficient is conducted to narrow the scope of the inspection fault. Finally, the validity and usefulness of the proposed GDMPM monitoring method are demonstrated through the Tennessee Eastman (TE) benchmark process.
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Song, Ke, Juan Li, Yuanqiang Zhu, Fang Ren, Lingcan Cao, and Zi-Gang Huang. "Altered Small-World Functional Network Topology in Patients with Optic Neuritis: A Resting-State fMRI Study." Disease Markers 2021 (June 14, 2021): 1–9. http://dx.doi.org/10.1155/2021/9948751.
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Aim. This study investigated changes in small-world topology and brain functional connectivity in patients with optic neuritis (ON) by resting-state functional magnetic resonance imaging (rs-fMRI) and based on graph theory. Methods. A total of 21 patients with ON (8 males and 13 females) and 21 matched healthy control subjects (8 males and 13 females) were enrolled and underwent rs-fMRI. Data were preprocessed and the brain was divided into 116 regions of interest. Small-world network parameters and area under the integral curve (AUC) were calculated from pairwise brain interval correlation coefficients. Differences in brain network parameter AUCs between the 2 groups were evaluated with the independent sample t -test, and changes in brain connection strength between ON patients and control subjects were assessed by network-based statistical analysis. Results. In the sparsity range from 0.08 to 0.48, both groups exhibited small-world attributes. Compared to the control group, global network efficiency, normalized clustering coefficient, and small-world value were higher whereas the clustering coefficient value was lower in ON patients. There were no differences in characteristic path length, local network efficiency, and normalized characteristic path length between groups. In addition, ON patients had lower brain functional connectivity strength among the rolandic operculum, medial superior frontal gyrus, insula, median cingulate and paracingulate gyri, amygdala, superior parietal gyrus, inferior parietal gyrus, supramarginal gyrus, angular gyrus, lenticular nucleus, pallidum, superior temporal gyrus, and cerebellum compared to the control group ( P < 0.05 ). Conclusion. Patients with ON show typical “small world” topology that differed from that detected in HC brain networks. The brain network in ON has a small-world attribute but shows reduced and abnormal connectivity compared to normal subjects and likely causes symptoms of cognitive impairment.
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Zhang, Chao, Chuang-Jin He, Shuai Li, Yu-Ling He, Xiao-Long Wang, Xiang-Yu Liu, and Lun Cheng. "A Hybrid Method to Diagnose 3D Rotor Eccentricity Faults in Synchronous Generators Based on ALIF_PE and KFCM." Mathematical Problems in Engineering 2021 (May 31, 2021): 1–14. http://dx.doi.org/10.1155/2021/5513881.
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This paper proposed a new hybrid diagnosis method for the generator’s 3D static eccentricity faults which include the axial eccentricity, the radial eccentricity, and the mixed eccentricity composed of the former two. Firstly, adaptive local iterative filtering (ALIF) method was used to decompose the vibration signals of the generator under eccentricity faults. Then, in order to figure out the intrinsic mode function (IMF) components with the upmost feature information, the correlation coefficient was calculated. Finally, the components’ permutation entropy (PE) is extracted to construct the eigenvector matrix which can be used to input the kernel fuzzy C-means (KFCM) algorithm to obtain the result of clustering. The result indicates that the classification coefficient based on ALIF and KFCM behaves closer to 1, while the average fuzzy entropy (FE) is closer to 0, showing that this method is able to detect different eccentricity faults more accurately.
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Zeng, Xia Ling. "Cluster-Based Intrusion Detection Model for Wireless Sensor Network." Applied Mechanics and Materials 631-632 (September 2014): 914–17. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.914.
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An intrusion detection system (IDS) using agent technology was designed for wireless sensor network of clustering structure. An IDS agent which includes two different agents was deployed in every node of network. One is local detection agent and another is global detection agent. They complete different tasks of detection. Based on Bluetooth communication technology, Bluetooth scattering network formation algorithm TPSF was employed to construct the cluster layer of sensor network and to finish task assignment of different agents. The TPSF algorithm was improved by limiting the role of nodes to lighten the complexity of nodes, so the IDS agents can work effectively and the safety coefficient of nodes is improved.