Unsupervised Learning Algorithms
by M. Emre Celebi /
2016 / English / PDF
14 MB Download
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.
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.