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Journal articles on the topic 'Micro-clustering'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Nakahara, Takanobu, Takeaki Uno, and Yukinobu Hamuro. "Prediction Model Using Micro-clustering." Procedia Computer Science 35 (2014): 1488–94. http://dx.doi.org/10.1016/j.procs.2014.08.231.

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Gupta, Mukul, Pradeep Kumar, and Bharat Bhasker. "Clustering of Micro-Messages Using Similarity Upper Approximation." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, no. 01 (2017): 53–79. http://dx.doi.org/10.1142/s0218488517500039.

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Microblogging platforms like Twitter, Tumblr and Plurk have radically changed our lives. The presence of millions of people has made these platforms a preferred channel for communication. A large amount of User Generated Content, on these platforms, has attracted researchers and practitioners to mine and extract information nuggets. For information extraction, clustering is an important and widely used mining operation. This paper addresses the issue of clustering of micro-messages and corresponding users based on the text content of micro-messages that reflect their primitive interest. In thi

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3

Lin Li. "New Hybrid Clustering Algorithm for Micro Blog." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 23 (2012): 788–95. http://dx.doi.org/10.4156/aiss.vol4.issue23.97.

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Widyastuti, Christine, Adri Senen, and Oktaria Handayani. "Micro-Spatial Electricity Load Forecasting Clustering Technique." E3S Web of Conferences 202 (2020): 11005. http://dx.doi.org/10.1051/e3sconf/202020211005.

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Low growth of electricity load forecast eliminates cost opportunity of electricity sale due to unserviceable load demands. Meanwhile, if it is exorbitant, it will cause over-investment and incriminate investment cost. Existing method of sector load is simplified and easy to implement. However, the accuracy tends to bias over one area of which data is limited and dynamic service area. Besides, the results of its forecast is macro-based, which means it is unable to show load centres in micro grids and failed to locate the distribution station. Therefore, we need micro-spatial load forecasting. B

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K, Shyam Sunder Reddy, and Shoba Bindu C. "MCDAStream: a real-time data stream clustering based on micro-cluster density and attraction." International Journal of Engineering & Technology 7, no. 2 (2018): 270. http://dx.doi.org/10.14419/ijet.v7i2.9051.

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Real-time data stream clustering has been widely used in many fields, and it can extract useful information from massive sets of data. Most of the existing density-based algorithms cluster the data streams based on the density within the micro-clusters. These algorithms completely omit the data density in the area between the micro-clusters and recluster the micro-clusters based on erroneous assumptions about the distribution of the data within and between the micro-clusters that lead to poor clustering results. This paper describes a novel density-based clustering algorithm for evolving data

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Augustine, Donia. "A Survey on Density based Micro-clustering Algorithms for Data Stream Clustering." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 1 (2017): 186–90. http://dx.doi.org/10.23956/ijarcsse/v7i1/0111.

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7

Chen, Xinquan, and Yirou Qiu. "A Combined Clustering Algorithm Based on ESynC Algorithm and a Merging Judgement Process of Micro-Clusters." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 03 (2021): 463–95. http://dx.doi.org/10.1142/s0218488521500203.

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The first synchronization clustering (SynC) algorithm based on an extensive Kuromato model was presented in 2010. In 2017, an effective synchronization clustering (ESynC) algorithm, inspired by SynC algorithm and a linear version of Vicsek model, was proposed. When facing complex data distributions, ESynC algorithm may regard an irregular and whole cluster as some micro-clusters. To conquer this shortcoming, a combined clustering algorithm based on ESynC algorithm and a merging judgement process of micro-clusters (CESynC) is presented. CESynC algorithm first uses ESynC algorithm to detect clus

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Murakami, Daisuke, and Yoshiki Yamagata. "Micro grids clustering for electricity sharing: an approach considering micro urban structure." Energy Procedia 142 (December 2017): 2748–53. http://dx.doi.org/10.1016/j.egypro.2017.12.220.

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Riyadh, Musaab, Norwati Mustapha, Md Nasir Sulaiman, and Nurfadhlina Binti Mohd Sharef. "CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/7523138.

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The rapid spreading of positioning devices leads to the generation of massive spatiotemporal trajectories data. In some scenarios, spatiotemporal data are received in stream manner. Clustering of stream data is beneficial for different applications such as traffic management and weather forecasting. In this article, an algorithm for Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life is proposed. The algorithm consists of two phases. There is the online phase where temporal micro clusters are used to store summarized spatiotemporal information for each group of similar

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10

Fazelabdolabadi, Babak, and Mohammad Hossein Golestan. "Towards Bayesian Quantification of Permeability in Micro-scale Porous Structures – The Database of Micro Networks." HighTech and Innovation Journal 1, no. 4 (2020): 148–60. http://dx.doi.org/10.28991/hij-2020-01-04-02.

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This article develops a Bayesian framework to quantify the absolute permeability of water in a porous structure from the geometry and clustering parameters of its underlying pore-throat network. These parameters include the network`s diameter, transivity, degree, centrality, assortativity, edge density, K-core decomposition, Kleinberg’s hub centrality scores, Kleinberg's authority centrality scores, length, and porosity. In addition, the incorporated clustering aspects of the networks have been determined with respect to several clustering criteria – edge betweenness, greedy optimization of mo

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Bennett, S., and C. O. R. Everard. "Absence of epidemicity of severe leptospirosis in Barbados." Epidemiology and Infection 106, no. 1 (1991): 151–56. http://dx.doi.org/10.1017/s095026880005651x.

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SUMMARYThe possibility of micro-epidemics of severe leptospirosis occurring on the island of Barbados was investigated by examining the space-time clustering of the disease in 212 laboratory-confirmed cases admitted to Queen Elizabeth Hospital, Bridgetown, over a 7-year period. A series of 109 patients with symptoms compatible with leptospirosis but shown to be otherwise by laboratory examination were also examined for comparison. No significant space-time clustering was found among the leptospirosis cases, indicating no evidence for micro-epidemics. By comparison, statistically significant cl

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Zhan, Caijuan, Gaetano Sardina, Enkeleida Lushi, and Luca Brandt. "Accumulation of motile elongated micro-organisms in turbulence." Journal of Fluid Mechanics 739 (December 13, 2013): 22–36. http://dx.doi.org/10.1017/jfm.2013.608.

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AbstractWe study the effect of turbulence on marine life by performing numerical simulations of motile micro-organisms, modelled as prolate spheroids, in isotropic homogeneous turbulence. We show that the clustering and patchiness observed in laminar flows, linear shear and vortex flows, are significantly reduced in a three-dimensional turbulent flow mainly because of the complex topology; elongated micro-organisms show some level of clustering in the case of swimmers without any preferential alignment whereas spherical swimmers remain uniformly distributed. Micro-organisms with one preferenti

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Michael, E. J. "Tourism Micro-Clusters." Tourism Economics 9, no. 2 (2003): 133–45. http://dx.doi.org/10.5367/000000003101298312.

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This paper presents an approach to regional development policy that may serve to stimulate the growth of tourism-based industries. The context for this discussion lies in the experiences of Australia's rural towns, but the applications seem broadly international. The paper explains the concepts of clustering in a model for micro-markets that is consistent with the global demands for competition. The focus is on the creation of economic and social opportunity in small communities through the development of clusters of complementary firms that can collectively deliver a bundle of attributes to m

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14

Karunaratne, Pasan, Shanika Karunasekera, and Aaron Harwood. "Distributed stream clustering using micro-clusters on Apache Storm." Journal of Parallel and Distributed Computing 108 (October 2017): 74–84. http://dx.doi.org/10.1016/j.jpdc.2016.06.004.

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15

Miao, Jia Jia, Guo You Chen, Le Wang, and Xue Lin Fang. "A Semantic-Based Algorithm for Microblogs Clustering." Applied Mechanics and Materials 278-280 (January 2013): 1174–77. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.1174.

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Microblogging has become a major tool for people to not only share information, but also to talk about current affairs. Has become the most popular content in the analysis, interested companies and researchers. We focus on the micro-blog clustering high-dimensional, high sparse, and proposed a new algorithm based on k-means-k frequent itemsets. In addition, the development of a method to capture long-term mutual information context knowledge in microblogging and algorithms are also designed to measure the conversation Similar. In order to support the new micro-blog clustering algorithm. Experi

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16

Varghese, Sanoj, Ambili Reveendran, V. senthil Kumar, Karthikeyan Tm, and Venkiteshan Ranganathan. "MICRO RAMAN SPECTROSCOPIC ANALYSIS ON BLOOD SERUM SAMPLES OF DUCTAL CARCINOMA PATIENTS." Asian Journal of Pharmaceutical and Clinical Research 11, no. 9 (2018): 176. http://dx.doi.org/10.22159/ajpcr.2018.v11i9.26806.

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Objective: Identification of biochemical changes in ductal cancer patient’s serum samples using micro Raman spectroscopy.Methods: Micro Raman spectroscopy was used for the identification of Raman shift bands. Data analysis was done using K-means clustering.Results: Micro Raman spectroscopic analysis of human breast cancer patient’s serum samples was done. Biochemicals present in the samples were identified from the peak evaluations. K-means clustering analysis was used to differentiate the biochemicals present in the samples.Conclusion: From the study, we conclude that Raman spectroscopy has t

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17

Amini, Amineh, and Teh Ying Wah. "On Density-Based Clustering Algorithms over Evolving Data Streams: A Summarization Paradigm." Applied Mechanics and Materials 263-266 (December 2012): 2234–37. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2234.

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Clustering is one of the prominent classes in the mining data streams. Among various clustering algorithms that have been developed, density-based method has the ability to discover arbitrary shape clusters, and to detect the outliers. Recently, various algorithms adopted density-based methods for clustering data streams. In this paper, we look into three remarkable algorithms in two groups of micro-clustering and grid-based including DenStream, D-Stream, and MR-Stream. We compare the algorithms based on evaluating algorithm performance and clustering quality metrics.

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18

Reddy, D. Vivekananda. "Expository Aspect Clustering Design For Shared Substance Among Micro-Clusters." International Journal for Research in Applied Science and Engineering Technology V, no. XI (2017): 1860–68. http://dx.doi.org/10.22214/ijraset.2017.11269.

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19

Hahsler, Michael, and Matthew Bolaos. "Clustering Data Streams Based on Shared Density between Micro-Clusters." IEEE Transactions on Knowledge and Data Engineering 28, no. 6 (2016): 1449–61. http://dx.doi.org/10.1109/tkde.2016.2522412.

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20

Tracht, Kirsten, Ann-Kathrin Onken, Phil Gralla, Joel Haji Emad, Niklas Kipry, and Peter Maaß. "Trend-specific clustering for micro mass production of linked parts." CIRP Annals 67, no. 1 (2018): 9–12. http://dx.doi.org/10.1016/j.cirp.2018.04.017.

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21

Luo, Zhongming, Yu Zhang, Zixuan Zhou, Xuan Bi, Haibin Wu, and Zhentao Xin. "FCMLSM Segmentation of Micro-Vessels in Slight Defocused Microscopic Images." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 6 (2019): 1073–79. http://dx.doi.org/10.20965/jaciii.2019.p1073.

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To address problems relating to microscopic micro-vessel images of living bodies, including poor vessel continuity, blurry boundaries between vessel edges and tissue and uneven field illuminance, and this paper put forward a fuzzy-clustering level-set segmentation algorithm. By this method, pre-treated micro-vessel images were segmented by the fuzzy c-means (FCM) clustering algorithm to obtain original contours of interesting areas in images. By the evolution equations of the improved level set function, accurate segmentation of microscopic micro-vessel images was realized. This method can eff

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22

Zhe, Qu, Wing Chung Yiu, Ho Yin Yip, et al. "Micro-RNA Clusters Integrate Evolutionary Constraints on Expression and Target Affinities: The miR-6/5/4/286/3/309 Cluster in Drosophila." Molecular Biology and Evolution 37, no. 10 (2020): 2955–65. http://dx.doi.org/10.1093/molbev/msaa146.

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Abstract A striking feature of micro-RNAs is that they are often clustered in the genomes of animals. The functional and evolutionary consequences of this clustering remain obscure. Here, we investigated a micro-RNA cluster miR-6/5/4/286/3/309 that is conserved across drosophilid lineages. Small RNA sequencing revealed expression of this micro-RNA cluster in Drosophila melanogaster leg discs, and conditional overexpression of the whole cluster resulted in leg appendage shortening. Transgenic overexpression lines expressing different combinations of micro-RNA cluster members were also construct

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23

WANG, Li-min, You ZHOU, Xu-ming HAN, Yi-zhang WANG, Jing-lin YU, and Shuai WANG. "Constraint Rules and Matching Micro-clusters Based Affinity Propagation Clustering Algorithm." Studies in Informatics and Control 29, no. 3 (2020): 353–62. http://dx.doi.org/10.24846/v29i3y202008.

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24

Laohakiat, Sirisup, Suphakant Phimoltares, and Chidchanok Lursinsap. "Hyper-cylindrical micro-clustering for streaming data with unscheduled data removals." Knowledge-Based Systems 99 (May 2016): 183–200. http://dx.doi.org/10.1016/j.knosys.2016.02.004.

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25

Lux, Thomas, and Michele Marchesi. "Volatility Clustering in Financial Markets: A Micro-Simulation of Interacting Agents." IFAC Proceedings Volumes 31, no. 16 (1998): 7–10. http://dx.doi.org/10.1016/s1474-6670(17)40450-2.

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26

Adhau, S. P., R. M. Moharil, and P. G. Adhau. "K-Means clustering technique applied to availability of micro hydro power." Sustainable Energy Technologies and Assessments 8 (December 2014): 191–201. http://dx.doi.org/10.1016/j.seta.2014.09.001.

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27

Loistl, Otto. "Emergence of macro-variables by evaluation and clustering of micro- activities." European Journal of Finance 21, no. 9 (2014): 691–713. http://dx.doi.org/10.1080/1351847x.2013.871737.

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28

Rosales-Méndez, Henry, and Yunior Ramírez-Cruz. "A new micro-objects-based evaluation measure for co-clustering algorithms." Pattern Recognition Letters 84 (December 2016): 142–48. http://dx.doi.org/10.1016/j.patrec.2016.09.002.

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29

Wang, Ping, Luobing Dong, yueshen xu, Wei Liu, and Ningning Jing. "Clustering-Based Emotion Recognition Micro-Service Cloud Framework for Mobile Computing." IEEE Access 8 (2020): 49695–704. http://dx.doi.org/10.1109/access.2020.2979898.

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30

Bittanti, Sergio, Simone Garatti, and Diego Liberati. "FROM DNA MICRO-ARRAYS TO DISEASE CLASSIFICATION: AN UNSUPERVISED CLUSTERING APPROACH." IFAC Proceedings Volumes 38, no. 1 (2005): 319–24. http://dx.doi.org/10.3182/20050703-6-cz-1902.02168.

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31

Bi, Qifang, Andrew S. Azman, Syed Moinuddin Satter, et al. "Micro-scale Spatial Clustering of Cholera Risk Factors in Urban Bangladesh." PLOS Neglected Tropical Diseases 10, no. 2 (2016): e0004400. http://dx.doi.org/10.1371/journal.pntd.0004400.

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Zhang, Shunxiang, Shiyao Zhang, Neil Y. Yen, and Guangli Zhu. "The Recommendation System of Micro-Blog Topic Based on User Clustering." Mobile Networks and Applications 22, no. 2 (2016): 228–39. http://dx.doi.org/10.1007/s11036-016-0790-9.

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Aupke, Phil, Andreas Kassler, Andreas Theocharis, Magnus Nilsson, and Michael Uelschen. "Quantifying Uncertainty for Predicting Renewable Energy Time Series Data Using Machine Learning." Engineering Proceedings 5, no. 1 (2021): 50. http://dx.doi.org/10.3390/engproc2021005050.

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Recently, there has been growing interest in using machine learning based methods for forecasting renewable energy generation using time-series prediction. Such forecasting is important in order to optimize energy management systems in future micro-grids that will integrate a large amount of solar power generation. However, predicting solar power generation is difficult due to the uncertainty of the solar irradiance and weather phenomena. In this paper, we quantify the impact of uncertainty of machine learning based time-series predictors on the forecast accuracy of renewable energy generation

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Tian, Chengjin, Jinguo Han, Yebing Tian, Bing Liu, Zhiqiang Gu, and Xintao Hu. "Simulation Analysis of Cluster Effect of High-Shear Low-Pressure Grinding with Flexible Abrasive Tools." Micromachines 12, no. 7 (2021): 827. http://dx.doi.org/10.3390/mi12070827.

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Based on the clustering effect of shear-thickening fluids (STFs), a high-shear low-pressure flexible grinding wheel has been developed. In order to explore the material removal mechanism, the coupled Eulerian—Lagrangian (CEL) method is adopted to simulate the novel grinding process. The simulation results show that particle clustering effects do occur at the tangential and bottom positions of the micro-convex peak when it instantaneously strikes the workpiece surface. The particle clusters drive the harder abrasive particles to resist the strong interactions of micro-convex peaks. The micro-co

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35

Yan, Guang Hui, and Ming Hao Ai. "A Micro-Cluster-Based Data Stream Clustering Method for P2P Traffic Classification." Applied Mechanics and Materials 263-266 (December 2012): 1121–26. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1121.

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Many machine learning techniques were proposed to classify P2P traffic and each with reasonable successes. But in the real P2P network environment, new communities of peers often attend and old communities of peers often leave. It requires the identification methods to be capable of coping with concept drift and updating the model incrementally. In this paper, we presented a concept-adapting algorithm MCStream which was based on streaming data mining techniques to identify P2P applications in Internet traffic. The MCStream used two micro-cluster structures, potential micro-cluster structures a

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36

Sharma, Desh Deepak, and S. N. Singh. "Aberration detection in electricity consumption using clustering technique." International Journal of Energy Sector Management 9, no. 4 (2015): 451–70. http://dx.doi.org/10.1108/ijesm-11-2014-0001.

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Purpose – This paper aims to detect abnormal energy uses which relate to undetected consumption, thefts, measurement errors, etc. The detection of irregular power consumption, with variation in irregularities, helps the electric utilities in planning and making strategies to transfer reliable and efficient electricity from generators to the end-users. Abnormal peak load demand is a kind of aberration that needs to be detected. Design/methodology/approach – This paper proposes a Density-Based Micro Spatial Clustering of Applications with Noise (DBMSCAN) clustering algorithm, which is implemente

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Wang, Zhi Qiang, Gui Ying Zhang, and Bin Liu. "Detection of Wear Condition of Micro Milling Cutters Based on Length Fractal Dimension." Applied Mechanics and Materials 577 (July 2014): 697–700. http://dx.doi.org/10.4028/www.scientific.net/amm.577.697.

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In this paper, a new method to realize online wear detection of micro-milling cutters based on length fractal dimension is proposed. On the basis of expression derivation of length fractal dimension, experiments are conducted. First, several cutters with different wear condition are chosen as reference samples. Their multi-section vibration signals in time-domain are collected and the clustering domain δ of each sample are obtained based on length fractal dimensions. Then, the vibration signals of tested cutters are monitored and analysed in time domain, thus their length fractal dimension are

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Rajesh S R, Kanniga E, and Sundararajan M. "A Fuzzy clustering Approach for Micro Aneurysm Lesion Detection in Diabetic Retinopathy." International Journal of Research in Pharmaceutical Sciences 11, SPL4 (2020): 503–11. http://dx.doi.org/10.26452/ijrps.v11ispl4.3890.

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Diabetic retinopathy (DR) be the significant difficulty of diabetes, and micro aneurysm (MA) is an earliest diabetic retinopathy lesion, making early detection of MA a key factor in diabetic retinopathy. DR is a direct or indirect effect on human vision caused by chronic diabetes. During its early stages DR is asymptomatic, and the late diagnosis leads to undeviating vision loss. The computer-assisted diagnosis helps with prompt and effective care, with the aid of medical photos. MA mark the beginning of DR making it a vital screening stage for this disorder. Diabetic retinopathy is a persiste

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Ghesmoune, Mohammed, Mustapha Lebbah, and Hanene Azzag. "Micro-Batching Growing Neural Gas for Clustering Data Streams Using Spark Streaming." Procedia Computer Science 53 (2015): 158–66. http://dx.doi.org/10.1016/j.procs.2015.07.290.

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40

Jabeur, Nafaâ. "A Firefly-inspired Micro and Macro Clustering Approach for Wireless Sensor Networks." Procedia Computer Science 98 (2016): 132–39. http://dx.doi.org/10.1016/j.procs.2016.09.021.

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Redon, Josep, Amparao Miralles, Jose V. Lozano, et al. "168 Clustering of cardiovascular risk factors with micro-albuminuria in essential hypertension." Journal of Hypertension 11, no. 5 (1993): S471. http://dx.doi.org/10.1097/00004872-199312050-00337.

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Peters, Jörg, Maximiliane Sievert, and Christoph Strupat. "Impacts of a Micro-Enterprise Clustering Programme on Firm Performance in Ghana." European Journal of Development Research 27, no. 1 (2014): 99–121. http://dx.doi.org/10.1057/ejdr.2014.18.

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Aligholian, Armin, Alireza Shahsavari, Emma M. Stewart, Ed Cortez, and Hamed Mohsenian-Rad. "Unsupervised Event Detection, Clustering, and Use Case Exposition in Micro-PMU Measurements." IEEE Transactions on Smart Grid 12, no. 4 (2021): 3624–36. http://dx.doi.org/10.1109/tsg.2021.3063088.

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Caraka, Rezzy Eko, Robert Kurniawan, Bahrul Ilmi Nasution, et al. "Micro, Small, and Medium Enterprises’ Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering." Sustainability 13, no. 14 (2021): 7807. http://dx.doi.org/10.3390/su13147807.

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The COVID-19 pandemic has caused effects in many sectors, including in businesses and enterprises. The most vulnerable businesses to COVID-19 are micro, small, and medium enterprises (MSMEs). Therefore, this paper aims to analyze the business vulnerability of MSMEs in Indonesia using the fuzzy spatial clustering approach. The fuzzy spatial clustering approach had been implemented to analyze the social vulnerability to natural hazards in Indonesia. Moreover, this study proposes the Flower Pollination Algorithm (FPA) to optimize the Fuzzy Geographically Weighted Clustering (FGWC) in order to clu

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Li, Xiao Xian, Wei Jie Li, and Liu Yun. "Research Based on the HowNet Semantic Expansion Micro-Blog Hot Topic Detection System." Advanced Materials Research 765-767 (September 2013): 1502–6. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1502.

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In this paper, we propose an improved topic detection method based on the HowNet semantic expansion. Using the HowNet to extent the VSM of micro-blog content, solve the problem that the similarity calculation is not accurate because of the length of the micro-blog text is too small and the gap of user comments number is too large through the two-step above. The similarity calculated accurately, would there be a good clustering effect. Put forward a more complete topic heat assessment methods that take into account the number of fans of the Micro-Bo users, the number of forwarding Micro-Bo text

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Sharmili, K. C., and A. Chilambuchelvan. "A Hybridization Approach for Optimal Feature Subset Selection in High Dimensional Data." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, no. 06 (2018): 949–70. http://dx.doi.org/10.1142/s0218488518500423.

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Feature subset selection assumes an essential part in the fields of data mining and machine learning. A good feature subset selection algorithm can adequately expel unimportant and repetitive elements and consider feature interaction. This not just paves the way to an understanding comprehension of the information, additionally enhances the execution of a learner by improving the generalization capacity and the interpretability of the learning model. Initially, the input micro array dataset is selected from the medical database. Then preprocessing step is done in the input micro array dataset.

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HU, TIANMING, CHEW LIM TAN, YONG TANG, SAM YUAN SUNG, HUI XIONG, and CHAO QU. "CO-CLUSTERING BIPARTITE WITH PATTERN PRESERVATION FOR TOPIC EXTRACTION." International Journal on Artificial Intelligence Tools 17, no. 01 (2008): 87–107. http://dx.doi.org/10.1142/s0218213008003790.

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The duality between document and word clustering naturally leads to the consideration of storing the document dataset in a bipartite. With documents and words modeled as vertices on two sides respectively, partitioning such a graph yields a co-clustering of words and documents. The topic of each cluster can then be represented by the top words and documents that have highest within-cluster degrees. However, such claims may fail if top words and documents are selected simply because they are very general and frequent. In addition, for those words and documents across several topics, it may not

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Li, Zhanjiang, and Chengrong Yang. "Establishment of the Credit Indicator System of Micro Enterprises Based on Support Vector Machine and R-Type Clustering." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/6390720.

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The micro enterprises’ credit indicators with credit identification ability are selected by the two classification models of Support Vector Machine for the first round of indicator selection and then for the second round of indicator selection, deleting credit indicators with redundant information by clustering variables through the principle of minimum sum of deviation squares. This paper provides a screening model for credit evaluation indicators of micro enterprises and uses credit data of 860 micro enterprises samples in Inner Mongolia in western China for application analysis. The test re

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Grimstad, Sidsel, and John Burgess. "Environmental sustainability and competitive advantage in a wine tourism micro-cluster." Management Research Review 37, no. 6 (2014): 553–73. http://dx.doi.org/10.1108/mrr-01-2013-0019.

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Abstract:

Purpose – The paper aims to examine the competitive advantage of the environmental behaviour at a firm level and micro-cluster level, building the analysis on Harts model of natural resource-based view of the firm and by using Brown et al.'s framework for analysing contextual resources that would provide locational advantage based on environmental behaviour. The case study examines the drivers and the obstacles to environmental action and demonstrates how clustering has been important in progressing a sustainability agenda. Design/methodology/approach – A case study of a single wine tourism cl

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Garatti, Simone, Sergio Bittanti, Diego Liberati, and Andrea Maffezzoli. "An unsupervised clustering approach for leukaemia classification based on DNA micro-arrays data." Intelligent Data Analysis 11, no. 2 (2007): 175–88. http://dx.doi.org/10.3233/ida-2007-11205.

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