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Unsupervised learning algorithms

Aydin, Kemal.

Unsupervised learning algorithms

Record Type:	Electronic resources : Monograph/item
Title/Author:	Unsupervised learning algorithmsedited by M. Emre Celebi, Kemal Aydin.
other author:	Celebi, M. Emre.
Published:	Cham :Springer International Publishing :2016.
Description:	x, 558 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	http://dx.doi.org/10.1007/978-3-319-24211-8
ISBN:	9783319242118$q(electronic bk.)

Unsupervised learning algorithms
Unsupervised learning algorithms[electronic resource] /edited by M. Emre Celebi, Kemal Aydin. - Cham :Springer International Publishing :2016. - x, 558 p. :ill. (some col.), digital ;24 cm.

Introduction -- Feature Construction -- Feature Extraction -- Feature Selection -- Association Rule Learning -- Clustering -- Anomaly/Novelty/Outlier Detection -- Evaluation of Unsupervised Learning -- Applications -- Conclusion.

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.

ISBN: 9783319242118$q(electronic bk.)

Standard No.: 10.1007/978-3-319-24211-8doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Unsupervised learning algorithms
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505 0 $a Introduction -- Feature Construction -- Feature Extraction -- Feature Selection -- Association Rule Learning -- Clustering -- Anomaly/Novelty/Outlier Detection -- Evaluation of Unsupervised Learning -- Applications -- Conclusion.
520 $a 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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