Band 11
Clustering and Information Retrieval
Aus der Reihe
Network Theory and Applications
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- Hardcover
- Taschenbuch ausgewählt
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Sprache:Englisch
Fr. 191.00
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
17.09.2011
Abbildungen
VIII, 330 p.
Herausgeber
Weili Wu + weitereVerlag
Springer UsSeitenzahl
330
Maße (L/B/H)
23.5/15.5/1.9 cm
Gewicht
517 g
Auflage
Softcover reprint of the original 1st edition 2004
Sprache
Englisch
ISBN
978-1-4613-7949-2
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.
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