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Books > Computing & IT > Applications of computing > Artificial intelligence
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Clustering and Information Retrieval (Paperback, Softcover reprint of the original 1st ed. 2004)
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Clustering and Information Retrieval (Paperback, Softcover reprint of the original 1st ed. 2004)
Series: Network Theory and Applications, 11
Expected to ship within 10 - 15 working days
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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."
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