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Unsupervised Learning Algorithms

Unsupervised Learning Algorithms

M. Emre Celebi • Kemal Aydin Editors

Unsupervised Learning Algorithms

123

Editors M. Emre Celebi Department of Computer Science Louisiana State University in Shreveport Shreveport, LA, USA

ISBN 978-3-319-24209-5 DOI 10.1007/978-3-319-24211-8

Kemal Aydin North American University Houston, TX, USA

ISBN 978-3-319-24211-8 (eBook)

Library of Congress Control Number: 2015060229 Springer Cham Heidelberg New York Dordrecht London © Springer International Publishing Switzerland 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www. springer.com)

Preface

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. 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. The difficulty of developing theoretically sound approaches that are amenable to objective evaluation has resulted in the proposal of numerous unsupervised learning algorithms over the past half-century. The goal of this volume is to summarize the state of the art in unsupervised learning. The intended audience includes researchers and practitioners who are increasingly using unsupervised learning algorithms to analyze their data. This volume opens with two chapters on anomaly detection. In “Anomaly Detection for Data with Spatial Attributes,” P. Deepak reviews anomaly detection techniques for spatial data developed in the data mining and statistics communities. The author presents a taxonomy of such techniques, describes the most representative ones, and discusses th