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Article type: Research Article
Authors: Omran, Mahamed G.H.a; * | Engelbrecht, Andries P.b | Salman, Ayedc
Affiliations: [a] Department of Computer Science, Gulf University for Science and Technology, Kuwait | [b] Department of Computer Science, School of Information Technology, University of Pretoria, Pretoria 0002, South Africa | [c] Department of Computer Engineering, Kuwait University, Kuwait
Correspondence: [*] Corresponding author. E-mail: mjomran@gmail.com.
Abstract: Data clustering is the process of identifying natural groupings or clusters within multidimensional data based on some similarity measure. Clustering is a fundamental process in many different disciplines. Hence, researchers from different fields are actively working on the clustering problem. This paper provides an overview of the different representative clustering methods. In addition, several clustering validations indices are shown. Furthermore, approaches to automatically determine the number of clusters are presented. Finally, application of different heuristic approaches to the clustering problem is also investigated.
Keywords: Clustering, clustering validation, hard clustering, fuzzy clustering, unsupervised learning
DOI: 10.3233/IDA-2007-11602
Journal: Intelligent Data Analysis, vol. 11, no. 6, pp. 583-605, 2007
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