Data Mining and Analysis_Fundamental Concepts and Algorithms_2014

【实例简介】
Data Mining and Analysis_Fundamental Concepts and Algorithms_2014
DATA MINING AND ANALYSIS The fundamental algorithms in data mining and analysis form the basis for the emerging field of data science, which includes automated meth ods to analyze patterns and models for all kinds of data, with applications ranging from scientific discovery to business intelligence and analytics This textbook for senior undergraduate and graduate data mining courses ides a broad yet in-depth over of data mining integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classifi ation. The book lays the basic foundations of these tasks, and also covers cutting-edge topics such as kernel methods, high-dimensional data analy sis, and complex graphs and networks. With its comprehensive coverage, algorithmic perspective, and wealth of examples, this book offers solid guidance in data mining for students, researchers, and practitioners alike Key Features Covers both core methods and cutting-edge research Algorithmic approach with open-source implementations Minimal prerequisites, as all key mathematical concepts are pre sented as is the intuition behind the formulas Short, self-contained chapters with class-tested examples and exer cises that allow for flexibility in designing a course and for easy reference Supplementary online resource containing lecture slides. videos project ideas, and more Mohammed J. Zaki is a Professor of Computer Science at Rensselaer Polytechnic Institute, Troy, New York. Wagner Meira Jr is Associate Professor of Computer Science at Univer sidade federal de minas gerais brazil DATA MINING AND ANALYSIS Foundations and Algorithms MOHAMMED. ZAKI Rensselaer Polytechnic Institute Troy New york WAGNER MEIRA, R Universidade Federal de Minas Gerais, Brazil CAMBRIDGE UNIVERSITY PRESS CAMBRIDGE UNIVERSITY PRESS 32 Avenue of the Americas, New York, NY 10013-2473, USA Cambridge University Press is part of the University of Cambridge It furthers the University's mission by disseminating knowledge in the pursuit of education, learning and research at the highest international levels of excellence www.cambridge.org nformationonthistitic:www.cambridgc.org/9780521766333 Mohammed J Zaki and Wagner meira, Jr 2014 This publication is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press First published 2014 Printed in the United States of America A catalog record for this publication is available from the british Library Library of Congress Cataloging in Publication Data Zaki. Mohammed 1971 Data mining and analysis: foundations and algorithms /Mohammed J. Zaki, Rcnssclacr Polytechnic Institute, Troy, New York, Wagner Meira, Jr, Universidade Federal de Minas gerais. brazil ISBN978-0-521-76633-3( hardback) 1. Data mining. I. Meira, Wagner, 1967-II. Title QA769D343Z36201 006.312dc23 2013037544 isbn 978-0-521-76633-3 Hardback Cambridge University Press has no responsibility for the persistence or accuracy of URI s for external or third-party Internet Weh sites referred to in this puhlication and does not guarantee that any content on such web sites is, or will remain accurate or appropriate Contents Preface page 1 Data mil d Analysis 1.1 Data matrix 1.2 Attributes 1.3 Data: Alge braic and geometric view 1. 4 Data: Probabilistic vi 1.5 Data Mining 1.6 Further Reading 1.7E 30 PART ONE: DATA ANALYSIS FOUNDATIONS 2 Numeric Attributes 2.1 Univariate analysis 2.2 Bivariate Analysis 42 2.3 Multivariate Analysis ata normalizati 2.5 Normal distribution 2.6 Further readin g 27E 3 Categorical Attributes 3.1 Univariate△nal 63 3.2 Bivariate anal te analy 82 3.4 Distance and Angle 3.5 Discretization 3.6 Furthcr Reading 3.7 Exercises 4 Graph Data 93 4.1 Graph Concepts 4.2 Topological Attr Contents 4.3 orality Analy 102 4.4 Graph models 4.5 Further Reading 132 4.6 Exercises 5 Kernel methods 5.1 Kcrncl matrix 138 5.2 Vector Kernels 144 5.3 Basic Kernel Operations in Feature Space 5.4 Kernels for Complex Objects 154 5.5 Further Reading 5.6 Exercises 161 6 High-dimensional Data 163 6. 1 High-dimensional objects 163 6.2 High-dimcnsional volumes 6.3 Hypersphere Inscribed within Hypercube 6.4 Volume of Thin Hypersphere Shell 169 6.5 Diagonals in Hyperspace 171 6.6 Density of the multivariate normal 172 6. 7 Appendix: Derivation of Hypersphere Volume 175 6. 8 Further Reading 6.9 Exerc 80 7 Dimensionality Reduction 183 7.1 Background 183 7.2 Principal Component Analysis 7.3 Kernel Principal Component Analysis 202 7. 4 Singular Value Decomposition 208 7.5 Further Reading 7.6 Exercises 214 PART TWO: FREQUENT PATTERN MINING 8 Itemset Mining 217 8.1 Itemsets and a ssociation rule 217 8.2 Itemset Mining Algorithms 8.3 Generating Association Ri 234 8.4 Further Reading 236 8.5E 237 9 Summarizing Itemsets 9. 1 Maximal and closed Frequent Itemsets 242 9.2 Mining Maximal Frcqucnt Itcmscts: GcnMax Algorithm 245 9.3 Mining Closed Frequent Itemsets: Charm algorithm 9. 4 Nonderivable itemsets 250 Further readir 9.6E 10 Sequence Mining 10.1F 259 10.2 Mining Frequent Sequences 260 10.3 Substring Mining via Suffix Trees 10.4 Further Reading 10.5 Exercises 11 Graph Pattern Mining 281 11. 1 Isomorphism and support 281 11.2 Candidate generation 11. The gSpan algorithm 11.4 Further Reading 297 11.5 Exercises 298 12 Pattern and rule assessment 12.1 Rulc and pattcrn assessment mcasurcs 302 12.2 Significance Testing and Confidence Intervals 317 12. 3 Further reading 329 12. 4 Exercises 329 PART THREE: CLUSTERING Representative-based Clustering 333 13.1 K-mcans algorithm 333 13.2 Kernel K-means 338 13.3 Expectation Maximization Clustering 13. 4 Further reading 360 13.s Exercis 14 Hierarchical Clustering............... 14.1 Preliminaries 14.2 Agglomerative Hierarchical Clustering 366 14.3 Furthcr Reading 14.4 Exercises and Projects 373 15 Density-based Clustering 375 15. 1 The dBscan algorithm 375 15. 2 Kernel Density Estimation 379 15.3 Density-based Clustering: DENCLUE 385 15.4 Further Reading 390 15.5 Exercises 391 16 Spectral and Graph Clustering 394 16. 1 Graphs and matrices 394 16.2 Clustering as Graph Cut 401 16.3 Markov clusterin 416 16. 4 Further reading 422 16.5 Exercises 423 Contents 17 Clustering Validation 425 17.1 External measures 425 17.2 Internal measures 441 17.3 Relative measures 17. 4 Further Reading 461 17.5 Exercises 462 PART FOUR: CLASSIFICATION 465 18 Probabilistic classification 467 18.1 Bayes Classifier 18.2 Naive Bayes Classifier 473 18.3 Further Reading 477 18.4 Exercises 19 Decision Tree Classifier 479 19.1 ecision trees 19.2 Decision Tree algorithm 483 19.3 Further re 494 19.4 Exercise 494 20 Linear discriminant analysis 20.1 Optimal linear discriminant 496 20.2 Kcrncl discriminant analysis 503 20.3 Further read 20.4 Exercises 21 Support Vector Machines 512 21.1 Linear Discriminants and margins 512 21.2 SVM: Linear and Separable Case 21.3 Soft Margin SVM: Linear and Nonseparable Case 522 21. 4 Kernel svm: Nonlinear Case 528 21.5 SVM Training Algorithms 22 Classification Assessment 546 22.1 Classification Performance measures 546 22.2 Classifier evaluation 22.3 Bias-Variance Decomposition 22.4 Further Reading 578 22.5 Exercise 579 Index 583 【实例截图】
【核心代码】