Data Clustering: Algorithms And Applications (chapman & Hall/crc Data Mining And Knowledge Discovery Series)
by Charu C. Aggarwal /
2013 / English / PDF
12.7 MB Download
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,
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
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.
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:
The book focuses on three primary aspects of data clustering:Methods
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
, 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 factorizationDomains
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
, 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 dataVariations and Insights
Variations and Insights, discussing important
variations of the clustering process, such as semisupervised
clustering, interactive clustering, multiview clustering,
cluster ensembles, and cluster validation
, 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.
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.
