TESI DOCTORAL - La Salle

1.2. Clustering in knowledge discovery and data mining
the data, not provided by an external source (Jain, Murty, and Flynn, 1999).
Being such a generic task, clustering has found applications in multiple research fields,
among which we can name the following few:
– information retrieval, where clustering has been applied for organizing the results
returned by a search engine in response to a users query (i.e. post-retrieval clustering)
(Tombros, Villa, and van Rijsbergen, 2002; Hearst, 2006), for refining ambiguous
queries input to retrieval systems (Käki, 2005), or for improving their performance
(van Rijsbergen, 1979).
– text mining, where browsing through large document collections is simplified if they
are previously clustered (Cutting et al., 1992; Steinbach, Karypis, and Kumar, 2004;
Dhillon, Fan, and Guan, 2001).
– computational genomics, where clustering of gene expression data from DNA microarray
experiments is applied for identifying the functionality of genes, finding out what
genes are co-regulated or distinguishing the important genes between abnormal and
normal tissues (Zhao and Karypis, 2003a; Jiang, Tang, and Zhang, 2004).
– ecomomics, where clustering economic and financial time series can be employed for
identifying i) areas or sectors for policy-making purposes, ii) structural similarities
in economic processes for economic forecasting, iii) stable dependencies for risk management
and investment management (Focardi, 2001), or iv) customer profiles and
customers-products relationships (Liu and Luo, 2005).
– computer vision, where clustering is applied for common procedures such as image
preprocessing (Jain, 1996), segmentation (Mancas-Thillou and Gosselin, 2007) and
matching (Miyajima and Ralescu, 1993).
Regardless of the application, the desired result of any clustering process is a maximally
representative partition of the data set, which usually corresponds to clusters with high
intra-cluster and low inter-cluster object similarities. In the quest for this goal, a myriad
of clustering methods have been proposed. With no claim of being exhaustive, the next
section presents an overview of some of the most relevant clustering methods, highlighting
some important concepts in this context.
1.2.1 Overview of clustering methods
Several excellent and extensive surveys on clustering can be found in the literature (Jain,
Murty, and Flynn, 1999; Berkhin, 2002; Kotsiantis and Pintelas, 2004; Xu and Wunsch II,
2005). Providing a detailed description of the existing clustering methods lies beyond the
scope of this work, so the reader interested in their ins and outs is referred to the previously
cited surveys and references therein. However, due to the central role of clustering processes
in this thesis, this section presents a brief description of several key issues in this context,
such as:
1. A categorization of clustering algorithms according to generic criteria.
2. A brief discussion on similarity measures, one of the central notions in clustering.
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