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Clustering and Information Retrieval

  • Weili Wu
  • Hui Xiong
  • Shashi Shekhar

Part of the Network Theory and Applications book series (NETA, volume 11)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Ricardo Baeza-Yates, Benjamín Bustos, Edgar Chávez, Norma Herrera, Gonzalo Navarro
    Pages 1-33
  3. Sudipto Guha, Rajeev Rastogi, Kyuseok Shim
    Pages 35-82
  4. Levent Ertöz, Michael Steinbach, Vipin Kumar
    Pages 83-103
  5. Ji He, Ah-Hwee Tan, Chew-Lim Tan, Sam-Yuan Sung
    Pages 105-133
  6. Steven Noel, Vijay Raghavan, C.-H. Henry Chu
    Pages 161-193
  7. Ji-Rong Wen, Hong-Jiang Zhang
    Pages 195-225
  8. Sam Y. Sung, Zhao Li, Tok W. Ling
    Pages 227-259
  9. Daniel J. Crichton, J. Steven Hughes, Sean Kelly
    Pages 261-298

About this book

Introduction

Clustering is an important technique for discovering relatively dense sub-regions or sub-spaces of a multi-dimension data distribution. Clus­ tering has been used in information retrieval for many different purposes, such as query expansion, document grouping, document indexing, and visualization of search results. In this book, we address issues of cluster­ ing algorithms, evaluation methodologies, applications, and architectures for information retrieval. The first two chapters discuss clustering algorithms. The chapter from Baeza-Yates et al. describes a clustering method for a general metric space which is a common model of data relevant to information retrieval. The chapter by Guha, Rastogi, and Shim presents a survey as well as detailed discussion of two clustering algorithms: CURE and ROCK for numeric data and categorical data respectively. Evaluation methodologies are addressed in the next two chapters. Ertoz et al. demonstrate the use of text retrieval benchmarks, such as TRECS, to evaluate clustering algorithms. He et al. provide objective measures of clustering quality in their chapter. Applications of clustering methods to information retrieval is ad­ dressed in the next four chapters. Chu et al. and Noel et al. explore feature selection using word stems, phrases, and link associations for document clustering and indexing. Wen et al. and Sung et al. discuss applications of clustering to user queries and data cleansing. Finally, we consider the problem of designing architectures for infor­ mation retrieval. Crichton, Hughes, and Kelly elaborate on the devel­ opment of a scientific data system architecture for information retrieval.

Keywords

algorithms architecture architectures clustering data mining database information information retrieval visualization

Authors and affiliations

  • Weili Wu
    • 1
  • Hui Xiong
    • 2
  • Shashi Shekhar
    • 2
  1. 1.Department of Computer ScienceThe University of Texas at DallasRichardsonUSA
  2. 2.Department of Computer Science and EngineeringUniversity of Minnesota - Twin CitiesMinneapolisUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4613-0227-8
  • Copyright Information Springer-Verlag US 2004
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-7949-2
  • Online ISBN 978-1-4613-0227-8
  • Series Print ISSN 1568-1696
  • Buy this book on publisher's site