Unsupervised Learning Algorithms (Record no. 81098)

000 -LEADER
fixed length control field 03666nam a22006015i 4500
001 - CONTROL NUMBER
control field 978-3-319-24211-8
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220801222732.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 160429s2016 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319242118
-- 978-3-319-24211-8
082 04 - CLASSIFICATION NUMBER
Call Number 621.382
245 10 - TITLE STATEMENT
Title Unsupervised Learning Algorithms
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2016.
300 ## - PHYSICAL DESCRIPTION
Number of Pages X, 558 p. 160 illus., 101 illus. in color.
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Introduction -- Feature Construction -- Feature Extraction -- Feature Selection -- Association Rule Learning -- Clustering -- Anomaly/Novelty/Outlier Detection -- Evaluation of Unsupervised Learning -- Applications -- Conclusion.
520 ## - SUMMARY, ETC.
Summary, etc 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.
700 1# - AUTHOR 2
Author 2 Celebi, M. Emre.
700 1# - AUTHOR 2
Author 2 Aydin, Kemal.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-319-24211-8
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2016.
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-- computer
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-- rdamedia
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-- online resource
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-- rdacarrier
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-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Telecommunication.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer networks .
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Pattern recognition systems.
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-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data mining.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Communications Engineering, Networks.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Communication Networks.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Automated Pattern Recognition.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Mining and Knowledge Discovery.
700 1# - AUTHOR 2
-- (orcid)0000-0002-2721-6317
-- https://orcid.org/0000-0002-2721-6317
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-- ZDB-2-ENG
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-- ZDB-2-SXE

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