ISBN: 9780521617932 - introduction to clustering large and high-dimensional data (9 Ergebnisse)

Paperback. Zustand: Very Good. Introduction to Clustering Large and High-Dimensional Data This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .…

Paperback. Zustand: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee.

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.

Zustand: Used. pp. 222.

Zustand: Used. pp. 222.

Zustand: New. In English.

Zustand: New. Focuses on a few of the important clustering algorithms in the context of information retrieval. Num Pages: 222 pages, Illustrations. BIC Classification: UN. Category: (P) Professional & Vocational. Dimension: 228 x 154 x 16. Weight in Grams: 308. . 2007. Illustrated. paperback. . . . . Books ship from the US and Ireland. …

Paperback. Zustand: Brand New. 1st edition. 205 pages. 8.75x5.75x0.50 inches. In Stock.

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.…