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Introduction to clustering large and high-dimensional data / Jacob Kogan.

By: Material type: TextTextPublication details: Cambridge ; New York : Cambridge University Press, 2007.Description: 1 online resource (xvi, 205 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780511257483
  • 0511257481
  • 0511254806
  • 9780511254802
  • 0521617936
  • 9780521617932
  • 0521852676
  • 9780521852678
  • 0511256981
  • 9780511256981
  • 9780511256479
  • 0511256477
  • 1280709944
  • 9781280709944
  • 9786610709946
  • 6610709947
  • 0511319606
  • 9780511319600
Subject(s): Genre/Form: Additional physical formats: Print version:: Introduction to clustering large and high-dimensional data.DDC classification:
  • 519.5/3 22
LOC classification:
  • QA278 .K594 2007eb
Online resources:
Contents:
Cover; Half-title; Title; Copyright; Dedication; Contents; Foreword; Preface; 1 Introduction and motivation; 2 Quadratic k-means algorithm; 3 BIRCH; 4 Spherical k-means algorithm; 5 Linear algebra techniques; 6 Information theoretic clustering; 7 Clustering with optimization techniques; 8 k-means clustering with divergences; 9 Assessment of clustering results; 10 Appendix: Optimization and linear algebra background; 11 Solutions to selected problems; Bibliography; Index.
Summary: 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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Electronic-Books Electronic-Books OPJGU Sonepat- Campus E-Books EBSCO Available

Includes bibliographical references (pages 189-201) and index.

Print version record.

Cover; Half-title; Title; Copyright; Dedication; Contents; Foreword; Preface; 1 Introduction and motivation; 2 Quadratic k-means algorithm; 3 BIRCH; 4 Spherical k-means algorithm; 5 Linear algebra techniques; 6 Information theoretic clustering; 7 Clustering with optimization techniques; 8 k-means clustering with divergences; 9 Assessment of clustering results; 10 Appendix: Optimization and linear algebra background; 11 Solutions to selected problems; Bibliography; Index.

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.

English.

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