Categorization of Data Clustering Techniques

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Author(s): Baoying Wang (Waynesburg University, USA), Imad Rahal (College of Saint Benedict, Saint John’s University, USA)and Richard Leipold (Waynesburg University, USA)
Copyright: 2008
Pages: 10
Source title: Handbook of Research on Public Information Technology
Source Author(s)/Editor(s): G. David Garson (North Carolina State University, USA)and Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59904-857-4.ch052

Keywords: Electronic Government / Government & Law / Information Science Reference

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Abstract

Data clustering is a discovery process that partitions a data set into groups (clusters) such that data points within the same group have high similarity while being very dissimilar to points in other groups (Han & Kamber, 2001). The ultimate goal of data clustering is to discover natural groupings in a set of patterns, points, or objects without prior knowledge of any class labels. In fact, in the machine-learning literature, data clustering is typically regarded as a form of unsupervised learning as opposed to supervised learning. In unsupervised learning or clustering, there is no training function as in supervised learning. There are many applications for data clustering including, but not limited to, pattern recognition, data analysis, data compression, image processing, understanding genomic data, and market-basket research.

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Categorization of Data Clustering Techniques

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