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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