This book summarizes the state-of-the-art in unsupervised learning.
The contributors discuss how with the proliferation of massive
amounts of unlabeled data, unsupervised learning algorithms, which
can automatically discover interesting and useful patterns in such
data, have gained popularity among researchers and practitioners.
The authors outline how these algorithms have found numerous
applications including pattern recognition, market basket analysis,
web mining, social network analysis, information retrieval,
recommender systems, market research, intrusion detection, and
fraud detection. They present how the difficulty of developing
theoretically sound approaches that are amenable to objective
evaluation have resulted in the proposal of numerous unsupervised
learning algorithms over the past half-century. The intended
audience includes researchers and practitioners who are
increasingly using unsupervised learning algorithms to analyze
their data. Topics of interest include anomaly detection,
clustering, feature extraction, and applications of unsupervised
learning. Each chapter is contributed by a leading expert in the
field.
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