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Data Clustering: Algorithms and Applications (Chapman &
Hall/CRC Data Mining and Knowledge Discovery Series)
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Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series (Book 31) Hardcover: 652
pages Publisher: Chapman and Hall/CRC; 1 edition (August 21, 2013) Language: English ISBN-10:
1466558210 ISBN-13: 978-1466558212 Product Dimensions: 7.2 x 1.5 x 10 inches
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Book Description
Research on the problem of clustering tends to be fragmented across the pattern recognition,
database, data mining, and machine learning communities. Addressing this problem in a unified
way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area
of clustering, from basic methods to more refined and complex data clustering approaches. It pays
special attention to recent issues in graphs, social networks, and other domains. The book focuses
on three primary aspects of data clustering: Methods, describing key techniques commonly used
for clustering, such as feature selection, agglomerative clustering, partitional clustering, densitybased
clustering, probabilistic clustering, grid-based clustering, spectral clustering, and
nonnegative matrix factorization Domains, covering methods used for different domains of data,
such as categorical data, text data, multimedia data, graph data, biological data, stream data,
uncertain data, time series clustering, high-dimensional clustering, and big data Variations and
Insights, discussing important variations of the clustering process, such as semisupervised
clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In
this book, top researchers from around the world explore the characteristics of clustering
problems in a variety of application areas. They also explain how to glean detailed insight from
the clustering process―including how to verify the quality of the underlying clusters―through
supervision, human intervention, or the automated generation of alternative clusters.

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