[EBOOK] Data Clustering: Algorithms and Applicat

Data Clustering: Algorithms and Applications (Chapman &
Hall/CRC Data Mining and Knowledge Discovery Series)
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Data Clustering: Algorithms and Applications (Chapman &
Hall/CRC Data Mining and Knowledge Discovery Series)
BOOK DETAIL
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business account 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, density-based 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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Data Clustering: Algorithms and Applications (Chapman &
Hall/CRC Data Mining and Knowledge Discovery Series)
BOOK DETAIL
Amazon Business : For business-only pricing, quantity discounts and FREE Shipping. Register a free
business account 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
Step By Step To Download Or Read Online
1. Click Button "DOWNLOAD" Or "READ ONLINE"
2. Sign Up To Acces "Data Clustering: Algorithms and Applications (Chapman & Hall/CRC Data Mining and Knowledge
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5. Enjoy and Happy Reading

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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(Chapman & Hall/CRC Data Mining and Knowledge
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