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Ria Christie Collections, Uxbridge, Vereinigtes Königreich
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Focuses on a few of the important clustering algorithms in the context of information retrieval.
Über die Autorin bzw. den Autor: 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.
Titel: Introduction to Clustering Large and ...
Verlag: Cambridge University Press
Erscheinungsdatum: 2006
Einband: Softcover
Zustand: New
Anbieter: Majestic Books, Hounslow, Vereinigtes Königreich
Zustand: New. pp. 222. Artikel-Nr. 7621326
Anzahl: 1 verfügbar
Anbieter: Romtrade Corp., STERLING HEIGHTS, MI, USA
Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide. Artikel-Nr. ABNR-140670
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
Paperback. Zustand: Brand New. 1st edition. 205 pages. 8.75x5.75x0.50 inches. In Stock. Artikel-Nr. x-0521617936
Anzahl: 2 verfügbar
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Artikel-Nr. 9780521617932
Anzahl: 1 verfügbar