Clustering and Information Retrieval

Weili Wu

ISBN 10: 1402076827 / ISBN 13: 9781402076824

Editore: Springer US, Springer US Nov 2003, 2003

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Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 340 pp. Englisch.

Codice articolo 9781402076824

Dati bibliografici

• Titolo: Clustering and Information Retrieval
• Editore: Springer US, Springer US Nov 2003
• Data di pubblicazione: 2003
• Lingua: Inglese
• ISBN 10: 1402076827
• ISBN 13: 9781402076824
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 241 L x 160 A x 23 P (millimetri) 241 millimetri di larghezza per 160 millimetri di altezza per 23 millimetri di profondità
• Peso: 676 grammi

Descrizione articolo

Riassunto

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.

Contenuti

Clustering in Metric Spaces with Applications to Information Retrieval.- Techniques for Clustering Massive Data Sets.- Finding Topics in Collections of Documents: A Shared Nearest Neighbor Approach.- On Quantitative Evaluation of Clustering Systems.- Techniques for Textual Document Indexing and Retrieval via Knowledge Sources and Data Mining.- Document Clustering, Visualization, and Retrieval via Link Mining.- Query Clustering in the Web Context.- Clustering Techniques for Large Database Cleansing.- A Science Data System Architecture for Information Retrieval.- Granular Computing for the Design of Information Retrieval Support Systems.