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Introduction to clustering large and high-dimensional data. (English) Zbl 1183.62106
Cambridge: Cambridge University Press (ISBN 978-0-521-61793-2/pbk; 0-521-85267-6/hbk; 978-0-511-25480-2/ebook). xvi, 205 p. (2007).
Publisher’s description: There is a growing need for a more automated systems of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, and the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI designs, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences.
Rather than providing a comprehensive coverage of the area, the book focuses on a few important clustering algorithms. A detailed and elementary description of the algorithms is provided in the beginning chapters, to be easily absorbed by undergraduates. Recent research results involving sophisticated mathematics are of interest for graduate students and research experts.

MSC:
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics
68P20 Information storage and retrieval of data
68T05 Learning and adaptive systems in artificial intelligence
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