Featureless Data Clustering

Introduction

Data clustering has a wide range of applicability ranging from ontology learning (Wong et al., 2006) and market research, to pattern recognition and image processing (Jain et al., 1999). Depending on the areas of applications, many different names such as cluster analysis, numerical taxonomy, automatic classification, botryology and typological analysis have been devised to refer to essentially the same practice of data clustering. Each application of data clustering can be characterised by two aspects, namely, the manner through which the clusters are formed, and the criteria that govern the formation of clusters. The first aspect relates to the choice of clustering algorithm while the second aspect dictates the type of semantic measure (i.e. similarity or relatedness) to be used. The choice of the clustering algorithms, and semantic measures very much depends on the data elements to be clustered, computational constraints, and also the desired results. In the past, data clustering have been particularly successful with certain types of data such as documents, software systems, lexical units, webpages, Uniform Resource Identifiers (URIs) and images. This gives rise to various sub-areas of clustering such as document clustering(Steinbach et al., 2000), software botryology(Tzerpos and Holt, 1998), term clustering(Wong et al., 2007), webpage clustering(Wang and Kitsuregawa, 2002), usage-based URI clustering(Mobasher et al., 1999) and clustering-based image segmentation(Jain et al. 1999).