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Clustering and Information Retrieval : Network Theory and Applications - Weili Wu

Clustering and Information Retrieval

Network Theory and Applications

By: Weili Wu (Editor), Shashi Shekhar (Editor), Hui Xiong (Editor)

Hardcover Published: 30th November 2003
ISBN: 9781402076824
Number Of Pages: 330

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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.

Forewordp. vii
Clustering in Metric Spaces with Applications to Information Retrievalp. 1
Techniques for Clustering Massive Data Setsp. 35
Finding Topics in Collections of Documents: A Shared Nearest Neighbor Approachp. 83
On Quantitative Evaluation of Clustering Systemsp. 105
Techniques for Textual Document Indexing and Retrieval via Knowledge Sources and Data Miningp. 135
Document Clustering, Visualization, and Retrieval via Link Miningp. 161
Query Clustering in the Web Contextp. 195
Clustering Techniques for Large Database Cleansingp. 227
A Science Data System Architecture for Information Retrievalp. 261
Granular Computing for the Design of Information Retrieval Support Systemsp. 299
Table of Contents provided by Ingram. All Rights Reserved.

ISBN: 9781402076824
ISBN-10: 1402076827
Series: Network Theory and Applications
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 330
Published: 30th November 2003
Publisher: Springer-Verlag New York Inc.
Country of Publication: US
Dimensions (cm): 23.5 x 15.5  x 2.57
Weight (kg): 1.44