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Novel Trends in Clustering
Abstract
Clustering or finding a natural grouping of a data set is essential for knowledge discovery in many applications. This chapter provides an overview on emerging trends within the vital research area of clustering including subspace and projected clustering, correlation clustering, semi-supervised clustering, spectral clustering and parameter-free clustering. To raise the awareness of the reader for the challenges associated with clustering, the chapter first provides a general problem specification and introduces basic clustering paradigms. The requirements from concrete example applications in life sciences and the web provide the motivation for the discussion of novel approaches to clustering. Thus, this chapter is intended to appeal to all those interested in the state-of-the art in clustering including basic researchers as well as practitioners.
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