Introduction to Clustering Large and High-Dimensional Data

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9780521852678: Introduction to Clustering Large and High-Dimensional Data

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.

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Book Description:

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.

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.

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Jacob Kogan
Editore: Cambridge University Press
ISBN 10: 0521852676 ISBN 13: 9780521852678
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Descrizione libro Cambridge University Press. Hardback. Condizione libro: new. BRAND NEW, Introduction to Clustering Large and High Dimensional Data, Jacob Kogan, 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. Codice libro della libreria B9780521852678

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Jacob Kogan
Editore: Cambridge Univ Pr (2007)
ISBN 10: 0521852676 ISBN 13: 9780521852678
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Descrizione libro Cambridge Univ Pr, 2007. Hardcover. Condizione libro: Brand New. 1st edition. 205 pages. 9.00x6.25x0.75 inches. In Stock. Codice libro della libreria __0521852676

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Jacob Kogan
Editore: CAMBRIDGE UNIVERSITY PRESS, United Kingdom (2006)
ISBN 10: 0521852676 ISBN 13: 9780521852678
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Descrizione libro CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2006. Hardback. Condizione libro: New. Language: English . Brand New Book. 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. Codice libro della libreria AAA9780521852678

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4.

Jacob Kogan
Editore: CAMBRIDGE UNIVERSITY PRESS, United Kingdom (2006)
ISBN 10: 0521852676 ISBN 13: 9780521852678
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Descrizione libro CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2006. Hardback. Condizione libro: New. Language: English . Brand New Book. 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. Codice libro della libreria AAA9780521852678

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JACOB KOGAN
ISBN 10: 0521852676 ISBN 13: 9780521852678
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Descrizione libro 2007. Hardback. Condizione libro: NEW. 9780521852678 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. Codice libro della libreria HTANDREE01229107

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