Clustering and Information Retrieval

1. Front Matter

   Pages i-viii

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2. Clustering in Metric Spaces with Applications to Information Retrieval

   • Ricardo Baeza-Yates, Benjamín Bustos, Edgar Chávez, Norma Herrera, Gonzalo Navarro

   Pages 1-33

3. Techniques for Clustering Massive Data Sets

   • Sudipto Guha, Rajeev Rastogi, Kyuseok Shim

   Pages 35-82

4. Finding Topics in Collections of Documents: A Shared Nearest Neighbor Approach

   • Levent Ertöz, Michael Steinbach, Vipin Kumar

   Pages 83-103

5. On Quantitative Evaluation of Clustering Systems

   • Ji He, Ah-Hwee Tan, Chew-Lim Tan, Sam-Yuan Sung

   Pages 105-133

6. Techniques for Textual Document Indexing and Retrieval via Knowledge Sources and Data Mining

   • Wesley W. Chu, Victor Zhenyu Liu, Wenlei Mao

   Pages 135-159

7. Document Clustering, Visualization, and Retrieval via Link Mining

   • Steven Noel, Vijay Raghavan, C.-H. Henry Chu

   Pages 161-193

8. Query Clustering in the Web Context

   • Ji-Rong Wen, Hong-Jiang Zhang

   Pages 195-225

9. Clustering Techniques for Large Database Cleansing

   • Sam Y. Sung, Zhao Li, Tok W. Ling

   Pages 227-259

10. A Science Data System Architecture for Information Retrieval

    • Daniel J. Crichton, J. Steven Hughes, Sean Kelly

    Pages 261-298

11. Granular Computing for the Design of Information Retrieval Support Systems

    • Y. Y. Yao

    Pages 299-329

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.

• algorithms
• architecture
• architectures
• clustering
• data mining
• database
• information
• information retrieval
• visualization

• Department of Computer Science, The University of Texas at Dallas, Richardson, USA

  Weili Wu

• Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, USA

  Hui Xiong, Shashi Shekhar

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