Description
There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, 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 design, 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.
Focuses on a few of the important clustering algorithms in the context of information retrieval.
About the Author
Jacob Kogan is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County. Dr. Kogan received his PhD in Mathematics from Weizmann Institute of Science, has held teaching and research positions at the University of Toronto and Purdue University. His research interests include Text and Data Mining, Optimization, Calculus of Variations, Optimal Control Theory, and Robust Stability of Control Systems. Dr. Kogan is the author of Bifurcations of Extremals in Optimal Control and Robust Stability and Convexity: An Introduction. Since 2001, he has also been affiliated with the Department of Computer Science and Electrical Engineering at UMBC. Dr. Kogan is a recipient of 2004-2005 Fulbright Fellowship to Israel. Together with Charles Nicholas of UMBC and Marc Teboulle of Tel-Aviv University he is co-editor of the volume Grouping Multidimensional Data: Recent Advances in Clustering.
Reviews
"...this book may serve as a useful reference for scientists and engineers who need to understand the concepts of clustering in general and/or to focus on text mining applications. It is also appropriate for students who are attending a course in pattern recognition, data mining, or classification and are interested in learning more about issues related to the k-means scheme for an undergraduate or master's thesis project. Last, it supplies very interesting material for instructors." Nicolas Lomenie, IAPR Newsletter
Book Information
ISBN 9780521617932
Author Jacob Kogan
Format Paperback
Page Count 222
Imprint Cambridge University Press
Publisher Cambridge University Press
Weight(grams) 307g
Dimensions(mm) 229mm * 153mm * 15mm
Focuses on a few of the important clustering algorithms in the context of information retrieval.
About the Author
Jacob Kogan is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County. Dr. Kogan received his PhD in Mathematics from Weizmann Institute of Science, has held teaching and research positions at the University of Toronto and Purdue University. His research interests include Text and Data Mining, Optimization, Calculus of Variations, Optimal Control Theory, and Robust Stability of Control Systems. Dr. Kogan is the author of Bifurcations of Extremals in Optimal Control and Robust Stability and Convexity: An Introduction. Since 2001, he has also been affiliated with the Department of Computer Science and Electrical Engineering at UMBC. Dr. Kogan is a recipient of 2004-2005 Fulbright Fellowship to Israel. Together with Charles Nicholas of UMBC and Marc Teboulle of Tel-Aviv University he is co-editor of the volume Grouping Multidimensional Data: Recent Advances in Clustering.
Reviews
"...this book may serve as a useful reference for scientists and engineers who need to understand the concepts of clustering in general and/or to focus on text mining applications. It is also appropriate for students who are attending a course in pattern recognition, data mining, or classification and are interested in learning more about issues related to the k-means scheme for an undergraduate or master's thesis project. Last, it supplies very interesting material for instructors." Nicolas Lomenie, IAPR Newsletter
Book Information
ISBN 9780521617932
Author Jacob Kogan
Format Paperback
Page Count 222
Imprint Cambridge University Press
Publisher Cambridge University Press
Weight(grams) 307g
Dimensions(mm) 229mm * 153mm * 15mm
