Unsupervised Learning Algorithms

by M Emre Celebi (Editor), Kemal Aydin (Editor)

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.

568 pages, Paperback

First published November 18, 2015

Reviews

[Paul · Rating 3 out of 5]

This is an interesting collection of papers on recent (in 2018) developments in unsupervised learning. As a practitioner and not a researcher, there are many interesting ideas worth exploring, good insights, and other points that push you towards ideas that could be applied. As a point of criticism on this volume, it’s wide-ranging and unlikely to be read cover to cover. Only some parts will be relevant to any one reader, and that makes it even more expensive than it already is. The articles being the output of recent research, there is also unlikely to exist any implementation in the framework that you need. That’s not an impossible hurdle to take but the adoption of the ideas in this book will take its time.