eprintid: 1412 rev_number: 14 eprint_status: archive userid: 2 dir: disk0/00/00/14/12 datestamp: 2015-02-16 14:40:43 lastmod: 2017-02-08 12:21:44 status_changed: 2015-04-27 12:10:51 type: article metadata_visibility: show creators_name: Xu, R. creators_name: WunschII, D. creators_id: rxu@umr.edu creators_id: dwunsch@ece.umr.edu corp_creators: Department of Electrical and Computer Engineering, University of Missouri-Rolla, Rolla, MO 65409 USA title: Survey of Clustering Algorithms ispublished: pub subjects: MP2_3 divisions: EPOS-P full_text_status: none abstract: Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed. date: 2005-05 date_type: published publication: IEEE Transactions on Neural Networks volume: 16 number: 3 publisher: Institute of Electrical and Electronics Engineers pagerange: 645-678 id_number: doi:10.1109/TNN.2005.845141 refereed: TRUE issn: 1045-9227 official_url: http://dx.doi.org/10.1109/TNN.2005.845141 access_IS-EPOS: limited owner: Publisher citation: Xu, R. and WunschII, D. (2005) Survey of Clustering Algorithms. IEEE Transactions on Neural Networks, 16 (3). pp. 645-678. DOI: https://doi.org/10.1109/TNN.2005.845141