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Unsupervised learning algorithms

Celebi, M. Emre.

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Unsupervised learning algorithms

Record Type:	Electronic resources : Monograph/item
Title/Author:	Unsupervised learning algorithms/ edited 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.
[NT 15003449]:	Introduction -- Feature Construction -- Feature Extraction -- Feature Selection -- Association Rule Learning -- Clustering -- Anomaly/Novelty/Outlier Detection -- Evaluation of Unsupervised Learning -- Applications -- Conclusion.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-24211-8
ISBN:	9783319242118

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

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

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Unsupervised learning algorithms
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245 0 0 $a Unsupervised learning algorithms $h [electronic resource] / $c edited by M. Emre Celebi, Kemal Aydin.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a x, 558 p. : $b ill. (some col.), digital ; $c 24 cm.
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.
650 0 $a Machine learning. $3 533906
650 0 $a Computer algorithms. $3 523872
650 1 4 $a Engineering. $3 586835
650 2 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Computer Communication Networks. $3 775497
650 2 4 $a Pattern Recognition. $3 891045
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
700 1 $a Celebi, M. Emre. $3 2055156
700 1 $a Aydin, Kemal. $3 2191486
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773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-24211-8
950 $a Engineering (Springer-11647)