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Clustering and Information Retrieval

Clustering and Information Retrieval

Autorzy
Wydawnictwo Springer-Verlag New York Inc.
Data wydania 17/09/2011
Liczba stron 330
Forma publikacji książka w miękkiej oprawie
Poziom zaawansowania Dla profesjonalistów, specjalistów i badaczy naukowych
ISBN 9781461379492
Kategorie Algorytmy i struktura danych, Odtwarzanie informacji, Sztuczna inteligencja
623.19 PLN (z VAT)
$170.03 / £129.29 / €146.99 /
Produkt na zamówienie
Przesyłka w 3-4 tygodnie
Ilość
Do schowka

Opis książki

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.

Clustering and Information Retrieval

Spis treści

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.

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