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Artykuły w czasopismach na temat „Overlapping Clusters”

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Data publikacji: 8 września 2023

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1

Mirzaie, Mansooreh, Ahmad Barani, Naser Nematbakkhsh, and Majid Mohammad-Beigi. "Bayesian-OverDBC: A Bayesian Density-Based Approach for Modeling Overlapping Clusters." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/187053.

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Although most research in density-based clustering algorithms focused on finding distinct clusters, many real-world applications (such as gene functions in a gene regulatory network) have inherently overlapping clusters. Even with overlapping features, density-based clustering methods do not define a probabilistic model of data. Therefore, it is hard to determine how “good” clustering, predicting, and clustering new data into existing clusters are. Therefore, a probability model for overlap density-based clustering is a critical need for large data analysis. In this paper, a new Bayesian densi

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Singh, Sukhminder. "Estimation in overlapping clusters." Communications in Statistics - Theory and Methods 17, no. 2 (1988): 613–21. http://dx.doi.org/10.1080/03610928808829643.

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Danganan, Alvincent Egonia, Ariel M. Sison, and Ruji P. Medina. "OCA: overlapping clustering application unsupervised approach for data analysis." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 3 (2019): 1471. http://dx.doi.org/10.11591/ijeecs.v14.i3.pp1471-1478.

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<p>In this paper, a new data analysis tool called Overlapping Clustering Application (OCA) was presented. It was developed to identify overlapping clusters and outliers in an unsupervised manner. The main function of OCA is composed of three phases. The first phase is the detection of the abnormal values(outliers) in the datasets using median absolute deviation. The second phase is to segment data objects into cluster using k-means algorithm. Finally, the last phase is the identification of overlapping clusters, it uses maxdist (maximum distance of data objects allowed in a cluster) as a

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Danganan, Alvincent E., and Edjie Malonzo De Los Reyes. "eHMCOKE: an enhanced overlapping clustering algorithm for data analysis." Bulletin of Electrical Engineering and Informatics 10, no. 4 (2021): 2212–22. http://dx.doi.org/10.11591/eei.v10i4.2547.

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Improved multi-cluster overlapping k-means extension (IMCOKE) uses median absolute deviation (MAD) in detecting outliers in datasets makes the algorithm more effective with regards to overlapping clustering. Nevertheless, analysis of the applied MAD positioning was not considered. In this paper, the incorporation of MAD used to detect outliers in the datasets was analyzed to determine the appropriate position in identifying the outlier before applying it in the clustering application. And the assumption of the study was the size of the cluster and cluster that are close to each other can led t

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Qing, Huan. "Studying Asymmetric Structure in Directed Networks by Overlapping and Non-Overlapping Models." Entropy 24, no. 9 (2022): 1216. http://dx.doi.org/10.3390/e24091216.

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We consider the problem of modeling and estimating communities in directed networks. Models to this problem in the previous literature always assume that the sending clusters and the receiving clusters have non-overlapping property or overlapping property simultaneously. However, previous models cannot model the directed network in which nodes in sending clusters have overlapping property, while nodes in receiving clusters have non-overlapping property, especially for the case when the number of sending clusters is no larger than that of the receiving clusters. This kind of directed network ex

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Vidojević, Filip, Dušan Džamić, and Miroslav Marić. "E-function for Fuzzy Clustering in Complex Networks." Ipsi Transactions on Internet research 18, no. 1 (2022): 17–21. http://dx.doi.org/10.58245/ipsi.tir.22jr.04.

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In many real-life situations, data consists of entities and the connections between them, which are naturally described by a complex network (graph). The structure of the network is often such that it is possible to group nodes based on the existence of connections between them, where such groups are called clusters (communities, modules). If the nodes are allowed to partially belong to clusters, they are called fuzzy (overlapping) clusters. There is a huge number of algorithms in the literature that perform fuzzy clustering, that is finds overlapping clusters, so a mechanism is needed to eval

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Wu, Mary, Byung Chul Ahn, and Chong Gun Kim. "A Channel Reuse Procedure in Clustering Sensor Networks." Applied Mechanics and Materials 284-287 (January 2013): 1981–85. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1981.

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Sensor nodes having the limited resource, energy efficiency is an important issue. Clustering on the sensor networks reduces the volume of inter-node communications and raises energy efficiency by transmitting the data collected from members by a cluster head to a sink node. But, due to radio frequency characteristics, interference and collision can occur between neighbor clusters, the resulted re-transmission is more energy consuming. The interference and collision occurred among adjacent clusters can be resolved by assigning non-overlapping channels among neighbor clusters. In this paper, we

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Alaqtash, Mohammad, Moayad A.Fadhil, and Ali F. Al-Azzawi. "A Modified Overlapping Partitioning Clustering Algorithm for Categorical Data Clustering." Bulletin of Electrical Engineering and Informatics 7, no. 1 (2018): 55–62. http://dx.doi.org/10.11591/eei.v7i1.896.

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Clustering is one of the important approaches for Clustering enables the grouping of unlabeled data by partitioning data into clusters with similar patterns. Over the past decades, many clustering algorithms have been developed for various clustering problems. An overlapping partitioning clustering (OPC) algorithm can only handle numerical data. Hence, novel clustering algorithms have been studied extensively to overcome this issue. By increasing the number of objects belonging to one cluster and distance between cluster centers, the study aimed to cluster the textual data type without losing

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Lee, Kyung-Soon. "Resampling Feedback Documents Using Overlapping Clusters." KIPS Transactions:PartB 16B, no. 3 (2009): 247–56. http://dx.doi.org/10.3745/kipstb.2009.16-b.3.247.

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Amdekar, S. J. "An Unbiased Estimator in Overlapping Clusters." Calcutta Statistical Association Bulletin 34, no. 3-4 (1985): 231–32. http://dx.doi.org/10.1177/0008068319850312.

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Reiss, Howard, and Richard K. Bowles. "Mapping volume scale for overlapping clusters." Journal of Chemical Physics 112, no. 3 (2000): 1390–94. http://dx.doi.org/10.1063/1.480692.

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Danganan, Alvincent E., and Regina P. Arceo. "Overlapping clustering with k-median extension algorithm: An effective approach for overlapping clustering." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (2022): 1607. http://dx.doi.org/10.11591/ijeecs.v26.i3.pp1607-1615.

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Most natural <span>world data involves overlapping communities where an object may belong to one or more clusters, referred to as overlapping clustering. However, it is worth mentioning that these algorithms have a significant drawback. Since some of the algorithm uses k-means, it also inherits the characteristics of being noise sensitive due to the arithmetic mean value which noisy data can considerably influence and affects the clustering algorithm by biasing the structure of obtained clusters. This paper proposed a new overlapping clustering algorithm named OCKMEx, which uses k-median

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Faccioli, L., F. Pacaud, J. L. Sauvageot, et al. "The XXL Survey." Astronomy & Astrophysics 620 (November 20, 2018): A9. http://dx.doi.org/10.1051/0004-6361/201832931.

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Aims. A well characterised detection pipeline is an important ingredient for X-ray cluster surveys. Methods. We present the final development of the XXL Survey pipeline. The pipeline optimally uses X-ray information by combining many overlapping observations of a source when possible, both for its detection and its characterisation. It can robustly detect and characterise several types of X-ray sources: AGNs (point-like), galaxy clusters (extended), galaxy clusters contaminated by a central AGN, and pairs of AGNs close on the sky. We perform a thorough suite of validation tests via realistic s

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Chen, Yi-Hui, Eric Jui-Lin Lu, and Ya-Wen Cheng. "Categorization of Multiple Documents Using Fuzzy Overlapping Clustering Based on Formal Concept Analysis." International Journal of Software Engineering and Knowledge Engineering 30, no. 05 (2020): 631–47. http://dx.doi.org/10.1142/s0218194020500229.

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Most clustering algorithms build disjoint clusters. However, clusters might be overlapped because documents may belong to two or more categories in the real world. For example, a paper discussing the Apple Watch may be categorized into either 3C, Fashion, or even Clothing and Shoes. Therefore, overlapping clustering algorithms have been studied such that a resource can be assigned to one or more clusters. Formal Concept Analysis (FCA), which has many practical applications in information science, has been used in disjoin clustering, but has not been studied in overlapping clustering. To make o

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15

Virmani, Deepali, Nikita Jain, Ketan Parikh, Shefali Upadhyaya, and Abhishek Srivastav. "Proficient Normalised Fuzzy K-Means With Initial Centroids Methodology." International Journal of Knowledge Discovery in Bioinformatics 8, no. 1 (2018): 42–59. http://dx.doi.org/10.4018/ijkdb.2018010104.

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This article describes how data is relevant and if it can be organized, linked with other data and grouped into a cluster. Clustering is the process of organizing a given set of objects into a set of disjoint groups called clusters. There are a number of clustering algorithms like k-means, k-medoids, normalized k-means, etc. So, the focus remains on efficiency and accuracy of algorithms. The focus is also on the time it takes for clustering and reducing overlapping between clusters. K-means is one of the simplest unsupervised learning algorithms that solves the well-known clustering problem. T

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Pérez-Suárez, Airel, José Fco Martínez-Trinidad, Jesús A. Carrasco-Ochoa, and José E. Medina-Pagola. "A dynamic clustering algorithm for building overlapping clusters." Intelligent Data Analysis 16, no. 2 (2012): 211–32. http://dx.doi.org/10.3233/ida-2012-0520.

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Lee, Woojung, and JoyceJiyoung Whang. "Cascading Behavior and Information Diffusion in Overlapping Clusters." Journal of KIISE 47, no. 4 (2020): 422–32. http://dx.doi.org/10.5626/jok.2020.47.4.422.

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Balaguer-Núñez, L., M. López del Fresno, E. Solano, et al. "Clusterix 2.0: a virtual observatory tool to estimate cluster membership probability." Monthly Notices of the Royal Astronomical Society 492, no. 4 (2020): 5811–43. http://dx.doi.org/10.1093/mnras/stz3610.

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ABSTRACT Clusterix 2.0 is a web-based, Virtual Observatory compliant, interactive tool for the determination of membership probabilities in stellar clusters based on proper-motion data using a fully non-parametric method. In an area occupied by a cluster, the frequency function is made up of two contributions: cluster and field stars. The tool performs an empirical determination of the frequency functions from the vector point diagram without relying on any previous assumption about their profiles. Clusterix 2.0 allows us to search the appropriate spatial areas in an interactive way until an o

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Chambers, Angela, David Atkinson, and Fiona Farr. "Centre for Applied Language Studies, University of Limerick, Ireland." Language Teaching 48, no. 2 (2015): 288–92. http://dx.doi.org/10.1017/s0261444814000445.

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The Centre for Applied Language Studies (CALS), founded in 1997, brings together researchers and postgraduate students from several disciplines in language studies, and is structured in three research clusters: New learning environments; Discourse, society and identity; and Plurilingualism and language policy. There is a certain amount of overlapping between the clusters, and several researchers are active in more than one cluster. Thus research in language teacher education is present both in the New learning environments cluster and in Discourse, society and identity. Corpus-based methodolog

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Kyoya, Shunki, and Kenji Yamanishi. "Summarizing Finite Mixture Model with Overlapping Quantification." Entropy 23, no. 11 (2021): 1503. http://dx.doi.org/10.3390/e23111503.

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Finite mixture models are widely used for modeling and clustering data. When they are used for clustering, they are often interpreted by regarding each component as one cluster. However, this assumption may be invalid when the components overlap. It leads to the issue of analyzing such overlaps to correctly understand the models. The primary purpose of this paper is to establish a theoretical framework for interpreting the overlapping mixture models by estimating how they overlap, using measures of information such as entropy and mutual information. This is achieved by merging components to re

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Azizah, Anestasya Nur, Tatik Widiharih, and Arief Rachman Hakim. "Kernel K-Means Clustering untuk Pengelompokan Sungai di Kota Semarang Berdasarkan Faktor Pencemaran Air." Jurnal Gaussian 11, no. 2 (2022): 228–36. http://dx.doi.org/10.14710/j.gauss.v11i2.35470.

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K-Means Clustering is one of the types of non-hierarchical cluster analysis which is frequently used, but has a weakness in processing data with non-linearly separable (do not have clear boundaries) characteristic and overlapping cluster, that is when visually the results of a cluster are between other clusters. The Gaussian Kernel Function in Kernel K-Means Clustering can be used to solve data with non-linearly separable characteristic and overlapping cluster. The difference between Kernel K-Means Clustering and K-Means lies on the input data that have to be plotted in a new dimension using k

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Peng, Yun, Shengyi Zhao, and Jizhan Liu. "Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method." Electronics 10, no. 22 (2021): 2813. http://dx.doi.org/10.3390/electronics10222813.

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Accurately extracting the grape cluster at the front of overlapping grape clusters is the primary problem of the grape-harvesting robot. To solve the difficult problem of identifying and segmenting the overlapping grape clusters in the cultivation environment of a trellis, a simple method based on the deep learning network and the idea of region growing is proposed. Firstly, the region of grape in an RGB image was obtained by the finely trained DeepLabV3+ model. The idea of transfer learning was adopted when training the network with a limited number of training sets. Then, the corresponding r

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23

Fang, Changjian, Dejun Mu, Zhenghong Deng, Jun Hu, and Chen-He Yi. "Fast detection of the fuzzy communities based on leader-driven algorithm." International Journal of Modern Physics B 32, no. 06 (2018): 1850058. http://dx.doi.org/10.1142/s0217979218500583.

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In this paper, we present the leader-driven algorithm (LDA) for learning community structure in networks. The algorithm allows one to find overlapping clusters in a network, an important aspect of real networks, especially social networks. The algorithm requires no input parameters and learns the number of clusters naturally from the network. It accomplishes this using leadership centrality in a clever manner. It identifies local minima of leadership centrality as followers which belong only to one cluster, and the remaining nodes are leaders which connect clusters. In this way, the number of

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Liu, Naiyuan, Dehao Shao, Jiaqi Tan, Qianqian Wan, and Tianyu Zhou. "Study of MLP-based classification of multi-cluster TPC signals." Theoretical and Natural Science 5, no. 1 (2023): 629–39. http://dx.doi.org/10.54254/2753-8818/5/20230405.

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This study focuses on the classification of multi-cluster events based on a parameterization of data from a time projection chamber using machine learning. Samples containing a mixture of single and overlapping two-cluster events, both in one and two dimensions, were studied using multi-layer perceptrons and other MVA algorithms provided in the Scikit-learn package. The classification was based on various sets of features and classification accuracies of up to 97% for 1D clusters and 97% for 2D clusters were obtained. This study demonstrates that the efficient classification of signals for fur

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Ciupala, Laura, Adrian DEACONU, and Delia SPRIDON. "ALGORITHM FOR MERGING AND INTERPOLATING CLUSTERS IN OVERLAPPING IMAGES." SERIES III - MATEMATICS, INFORMATICS, PHYSICS 13(62), no. 2 (2021): 697–704. http://dx.doi.org/10.31926/but.mif.2020.13.62.2.25.

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An image overlapping algorithm, taking into account certain properties of objects identified in the images (average intensity, movement speed, etc) is proposed. The algorithm minimizes both memory and time complexity and it can be used in various applications, especially in medical imaging analysis. The idea behind the proposed algorithm is surface merging and interpolation.

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Khandekar, Rohit, Guy Kortsarz, and Vahab Mirrokni. "On the Advantage of Overlapping Clusters for Minimizing Conductance." Algorithmica 69, no. 4 (2013): 844–63. http://dx.doi.org/10.1007/s00453-013-9761-8.

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Galadí-Enríquez, D., C. Jordi, E. Trullols, J. Guibert, K. P. Tian, and J. L. Zhao. "The overlapping open clusters NGC 1750 and NGC 1758." Astronomy and Astrophysics Supplement Series 131, no. 2 (1998): 239–58. http://dx.doi.org/10.1051/aas:1998433.

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Kobayashi, Mei, and Masaki Aono. "Exploring overlapping clusters using dynamic re-scaling and sampling." Knowledge and Information Systems 10, no. 3 (2006): 295–313. http://dx.doi.org/10.1007/s10115-006-0005-y.

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Osahan, Sukhminder S. "Overlapping Clusters of Tuberculosis Contacts: An Improved Sampling Strategy." Biometrical Journal 39, no. 6 (1997): 689–97. http://dx.doi.org/10.1002/bimj.4710390607.

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Aguena, M., C. Benoist, L. N. da Costa, et al. "The WaZP galaxy cluster sample of the dark energy survey year 1." Monthly Notices of the Royal Astronomical Society 502, no. 3 (2021): 4435–56. http://dx.doi.org/10.1093/mnras/stab264.

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ABSTRACT We present a new (2+1)D galaxy cluster finder based on photometric redshifts called Wavelet Z Photometric (WaZP) applied to DES first year (Y1A1) data. The results are compared to clusters detected by the South Pole Telescope (SPT) survey and the redMaPPer cluster finder, the latter based on the same photometric data. WaZP searches for clusters in wavelet-based density maps of galaxies selected in photometric redshift space without any assumption on the cluster galaxy populations. The comparison to other cluster samples was performed with a matching algorithm based on angular proximit

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Lai, Daphne Teck Ching, and Yuji Sato. "An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †." Algorithms 14, no. 11 (2021): 338. http://dx.doi.org/10.3390/a14110338.

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Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work a

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Fu, Xiaolan. "Research on Financial Data Query and Distribution Scheme Based on SQL Database." Wireless Communications and Mobile Computing 2020 (November 26, 2020): 1–7. http://dx.doi.org/10.1155/2020/8819083.

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With the advance of optimization and merger colleges and universities, a university often contains more than one campus. The traditional centralized education management system has been unable to meet the needs of use. The model detects the intrusion by dividing the clusters in the clustering result into normal clusters and abnormal clusters and analyzing the weighted average density of object x to be detected in each cluster and the weighted overlapping distance of x and each centre point. We verified the intrusion detection performance of the model on the KDD Cup 99 dataset. The experimental

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Al-Sabaawi, Ali M. Ahmed, Hacer Karacan, and Yusuf Erkan Yenice. "A Novel Overlapping Method to Alleviate the Cold-Start Problem in Recommendation Systems." International Journal of Software Engineering and Knowledge Engineering 31, no. 09 (2021): 1277–97. http://dx.doi.org/10.1142/s0218194021500418.

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Recommendation systems (RSs) are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The main objective of RSs is to tool up users with desired items that meet their preferences. A major problem in RSs is called: “cold-start”; it is a potential problem called so in computer-based information systems which comprises a degree of automated data modeling. Particularly, it concerns the issue in which the system cannot draw any inferences nor have it yet gathered sufficient informat

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Zhang, Jian, and Ling Shen. "An Improved Fuzzyc-Means Clustering Algorithm Based on Shadowed Sets and PSO." Computational Intelligence and Neuroscience 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/368628.

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To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzyc-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds t

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Kimani-Njogu, Susan W., William A. Overholt, James Woolley, and Annette Walker. "Biosystematics of the Cotesia flavipes species complex (Hymenoptera: Braconidae): morphometrics of selected allopatric populations." Bulletin of Entomological Research 87, no. 1 (1997): 61–66. http://dx.doi.org/10.1017/s0007485300036361.

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AbstractMorphometric studies of allopatric populations of the Cotesia flavipes species complex representing three putative species; C. flavipes Cameron, C. sesamiae (Cameron) and C. chilonis (Matsumura), were conducted. Sixteen characters were measured. Principal component analysis separated the complex into three somewhat overlapping groups that corresponded well with previous concepts of the species. Canonical variate analysis separated the complex into three distinct clusters with populations from Africa together, populations from Asia and the Neotropics forming a second cluster, and materi

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Yu, Linsheng, Fang Liu, Sisi Huang, Shoudong Bi, Chao Zong, and Tianshu Wang. "Morphmetric analysis of apis cerana populations in Huangshan, China." Journal of Apicultural Science 57, no. 2 (2013): 117–24. http://dx.doi.org/10.2478/jas-2013-0022.

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Abstract Honey bees (Apis cerana Fabricius) were collected from 195 colonies at seven different localities spanning the main beekeeping areas in Huangshan. Morphometric methods were used to measure seven standard morphometric characters, and these bees were compared to samples from the Henan, Shandong, and Yunnan provinces. Principal component analysis of the total Huangshan database yielded two clusters: bees from Jinxian and Jixixian, and those from other localities. Within the latter cluster, discriminant and hierarchical cluster analyses revealed overlapping regional sub-clusters: bees fro

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Richette, Pascal, Marijn Vis, Sarah Ohrndorf, et al. "Identification of PsA phenotypes with machine learning analytics using data from two phase III clinical trials of guselkumab in a bio-naïve population of patients with PsA." RMD Open 9, no. 1 (2023): e002934. http://dx.doi.org/10.1136/rmdopen-2022-002934.

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ObjectivesPsoriatic arthritis (PsA) phenotypes are typically defined by their clinical components, which may not reflect patients’ overlapping symptoms. This post hoc analysis aimed to identify hypothesis-free PsA phenotype clusters using machine learning to analyse data from the phase III DISCOVER-1/DISCOVER-2 clinical trials.MethodsPooled data from bio-naïve patients with active PsA receiving guselkumab 100 mg every 8/4 weeks were retrospectively analysed. Non-negative matrix factorisation was applied as an unsupervised machine learning technique to identify PsA phenotype clusters; baseline

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Makogonenko, Evgeny, Olav Andersen, Irina Mikhailenko, et al. "Localization of the low-density lipoprotein receptor-related protein regions involved in binding to the A2 domain of coagulation factor VIII." Thrombosis and Haemostasis 98, no. 12 (2007): 1170–81. http://dx.doi.org/10.1160/th07-05-0353.

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SummaryCatabolism of coagulation factorVIII (FVIII) is mediated by lowdensity lipoprotein receptor-related protein (LRP). The ligandbinding sites of LRP are formed by complement-type repeats (CR), and CR clusters II and IV bind most of LRP ligands. FVIII contains two major LRP-binding sites located in the A2 and A3 domains. This study was aimed to identify specific complementtype repeats of LRP involved in interaction with the A2 site and to probe their functional importance in A2 catabolism. We generated individual LRP clusters II, III and IV, along with nine overlapping CR triplets encompass

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Aisina, Dana, Raigul Niyazova, Shara Atambayeva, and Anatoliy Ivashchenko. "Prediction of clusters of miRNA binding sites in mRNA candidate genes of breast cancer subtypes." PeerJ 7 (November 13, 2019): e8049. http://dx.doi.org/10.7717/peerj.8049.

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The development of breast cancer (BC) subtypes is controlled by distinct sets of candidate genes, and the expression of these genes is regulated by the binding of their mRNAs with miRNAs. Predicting miRNA associations and target genes is thus essential when studying breast cancer. The MirTarget program identifies the initiation of miRNA binding to mRNA, the localization of miRNA binding sites in mRNA regions, and the free energy from the binding of all miRNA nucleotides with mRNA. Candidate gene mRNAs have clusters (miRNA binding sites with overlapping nucleotide sequences). mRNAs of EPOR, MAZ

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Laskhmaiah, K., S. Murali Krishna, and B. Eswara Reddy. "An Optimized K-means with Density and Distance-Based Clustering Algorithm for Multidimensional Spatial Databases." International Journal of Computer Network and Information Security 13, no. 6 (2021): 70–82. http://dx.doi.org/10.5815/ijcnis.2021.06.06.

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From massive and complex spatial database, the useful information and knowledge are extracted using spatial data mining. To analyze the complexity, efficient clustering algorithm for spatial database has been used in this area of research. The geographic areas containing spatial points are discovered using clustering methods in many applications. With spatial attributes, the spatial clustering problem have been designed using many approaches, but non-overlapping constraints are not considered. Most existing data mining algorithms suffer in high dimensions. With non-overlapping named as Non Ove

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Fan, Jiande, Weixin Xie, and Haocui Du. "A Robust Multi-Sensor Data Fusion Clustering Algorithm Based on Density Peaks." Sensors 20, no. 1 (2019): 238. http://dx.doi.org/10.3390/s20010238.

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In this paper, a novel multi-sensor clustering algorithm, based on the density peaks clustering (DPC) algorithm, is proposed to address the multi-sensor data fusion (MSDF) problem. The MSDF problem is raised in the multi-sensor target detection (MSTD) context and corresponds to clustering observations of multiple sensors, without prior information on clutter. During the clustering process, the data points from the same sensor cannot be grouped into the same cluster, which is called the cannot link (CL) constraint; the size of each cluster should be within a certain range; and overlapping clust

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Ouchicha, C., O. Ammor, and M. Meknassi. "A New Validity Index in Overlapping Clusters for Medical Images." Automatic Control and Computer Sciences 54, no. 3 (2020): 238–48. http://dx.doi.org/10.3103/s0146411620030050.

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Wang, Rui-Yan, Zhen-Yu Shi, Jin-Chun Chen, and Guo-Qiang Chen. "Cloning Large Gene Clusters fromE. coliUsingin VitroSingle-Strand Overlapping Annealing." ACS Synthetic Biology 1, no. 7 (2012): 291–95. http://dx.doi.org/10.1021/sb300025d.

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Vats, Divyanshu, and José M. F. Moura. "Finding Non-Overlapping Clusters for Generalized Inference Over Graphical Models." IEEE Transactions on Signal Processing 60, no. 12 (2012): 6368–81. http://dx.doi.org/10.1109/tsp.2012.2214216.

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Zhang, Hongli, Yang Gao, and Yue Zhang. "Overlapping communities from dense disjoint and high total degree clusters." Physica A: Statistical Mechanics and its Applications 496 (April 2018): 286–98. http://dx.doi.org/10.1016/j.physa.2017.12.146.

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Scripps, Jerry, and Pang-Ning Tan. "Constrained overlapping clusters: minimizing the negative effects of bridge-nodes." Statistical Analysis and Data Mining: The ASA Data Science Journal 3, no. 1 (2009): 20–37. http://dx.doi.org/10.1002/sam.10066.

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Himmelfarb, Sarah Talia, Nell Bond, Adaora Okoli, et al. "31. Post-ebola Syndrome Presents with Multiple Overlapping Symptom Clusters: Evidence from an Ongoing Cohort Study in Eastern Sierra Leone." Open Forum Infectious Diseases 7, Supplement_1 (2020): S16—S17. http://dx.doi.org/10.1093/ofid/ofaa417.030.

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Abstract Background Since the outbreak of Ebola Virus Disease (EVD) in West Africa from 2013–2016, a large cohort of survivors with persistent health complaints has emerged. This constellation of issues is termed post-Ebola syndrome. Here we characterize the symptoms and physical exam findings of this syndrome in a cohort of survivors from Sierra Leone 2.6 years after resolution of disease. Ebola survivors present with clusters of symptoms that represent sub phenotypes of post-Ebola syndrome Methods Potential survivor participants in Eastern Sierra Leone were identified and recruited through t

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Bettaieb, Afef, Nabila Filali, Taoufik Filali, and Habib Ben Aissia. "An efficient algorithm for overlapping bubbles segmentation." Computer Optics 44, no. 3 (2020): 363–74. http://dx.doi.org/10.18287/2412-6179-co-605.

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Image processing is an effective method for characterizing various two-phase gas/liquid flow systems. However, bubbly flows at a high void fraction impose significant challenges such as diverse bubble shapes and sizes, large overlapping bubble clusters occurrence, as well as out-of-focus bubbles. This study describes an efficient multi-level image processing algorithm for highly overlapping bubbles recognition. The proposed approach performs mainly in three steps: overlapping bubbles classification, contour segmentation and arcs grouping for bubble reconstruction. In the first step, we classif

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Castlebury, Frank D., Mark J. Hilsenroth, Leonard Handler, and Thomas W. Durham. "Use of the MMPI-2 Personality Disorder Scales in the Assessment of DSM-IV Antisocial, Borderline, and Narcissistic Personality Disorders." Assessment 4, no. 2 (1997): 155–68. http://dx.doi.org/10.1177/107319119700400205.

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This study explored the diagnostic utility of the MMPI-2 Personality Disorder (MMPI-2 PD) scales to correctly classify three Cluster B Personality Disorders (Antisocial, Borderline, and Narcissistic Personality Disorder). Classification was compared against the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) chart diagnoses checked for interrater agreement. MMPI-2 PD scale scores for 53 outpatients diagnosed with a Cluster B Personality Disorder were contrasted with an Other Personality Disorder group ( n = 20) and a nonclinical population ( n = 67). Scores

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Jeong, Hoyeon, Yoonbee Kim, Yi-Sue Jung, Dae Ryong Kang, and Young-Rae Cho. "Entropy-Based Graph Clustering of PPI Networks for Predicting Overlapping Functional Modules of Proteins." Entropy 23, no. 10 (2021): 1271. http://dx.doi.org/10.3390/e23101271.

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Functional modules can be predicted using genome-wide protein–protein interactions (PPIs) from a systematic perspective. Various graph clustering algorithms have been applied to PPI networks for this task. In particular, the detection of overlapping clusters is necessary because a protein is involved in multiple functions under different conditions. graph entropy (GE) is a novel metric to assess the quality of clusters in a large, complex network. In this study, the unweighted and weighted GE algorithm is evaluated to prove the validity of predicting function modules. To measure clustering acc

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