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S Sumathi, S.N Sivanandam Introduction to Data Mining and its Applications Studies in Computational Intelligence, Volume 29 Editor-in-chief Prof Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul Newelska 01-447 Warsaw Poland E-mail: kacprzyk@ibspan.waw.pl Further volumes of this series can be found on our homepage: springer.com Vol 12 Jonathan Lawry Modelling and Reasoning with Vague Concepts, 2006 ISBN 0-387-29056-7 Vol 13 Nadia Nedjah, Ajith Abraham, Luiza de Macedo Mourelle (Eds.) Genetic Systems Programming, 2006 ISBN 3-540-29849-5 Vol 14 Spiros Sirmakessis (Ed.) Adaptive and Personalized Semantic Web, 2006 ISBN 3-540-30605-6 Vol 15 Lei Zhi Chen, Sing Kiong Nguang, Xiao Dong Chen Modelling and Optimization of Biotechnological Processes, 2006 ISBN 3-540-30634-X Vol 16 Yaochu Jin (Ed.) Multi-Objective Machine Learning, 2006 ISBN 3-540-30676-5 Vol 17 Te-Ming Huang, Vojislav Kecman, Ivica Kopriva Kernel Based Algorithms for Mining Huge Data Sets, 2006 ISBN 3-540-31681-7 Vol 18 Chang Wook Ahn Advances in Evolutionary Algorithms, 2006 ISBN 3-540-31758-9 Vol 19 Ajita Ichalkaranje, Nikhil Ichalkaranje, Lakhmi C Jain (Eds.) Vol 21 Câ ndida Ferreira Gene Expression on Programming: Mathematical Modeling by an Artificial Intelligence, 2006 ISBN 3-540-32796-7 Vol 22 N Nedjah, E Alba, L de Macedo Mourelle (Eds.) Parallel Evolutionary Computations, 2006 ISBN 3-540-32837-8 Vol 23 M Last, Z Volkovich, A Kandel (Eds.) Algorithmic Techniques for Data Mining, 2006 ISBN 3-540-33880-2 Vol 24 Alakananda Bhattacharya, Amit Konar, Ajit K Mandal Parallel and Distributed Logic Programming, 2006 ISBN 3-540-33458-0 Vol 25 Zoltá n É sik, Carlos Martín-Vide, Victor Mitrana (Eds.) Recent Advances in Formal Languages and Applications, 2006 ISBN 3-540-33460-2 Vol 26 Nadia Nedjah, Luiza de Macedo Mourelle (Eds.) Swarm Intelligent Systems, 2006 ISBN 3-540-33868-3 Vol 27 Vassilis G Kaburlasos Towards a Unified Modeling and KnowledgeRepresentation based on Lattice Theory, 2006 ISBN 3-540-34169-2 Vol 28 Brahim Chaib-draa, Jö rg P Mü ller (Eds.) Multiagent based Supply Chain Management, 2006 ISBN 3-540-33875-6 Intelligent Paradigms for Assistive and Preventive Healthcare, 2006 Vol 29 S Sumathi, S.N Sivanandam ISBN 3-540-31762-7 2006 ISBN 3-540-34350-4 Vol 20 Wojciech Penczek, Agata Półrola Advances in Verification of Time Petri Nets and Timed Automata, 2006 ISBN 3-540-32869-6 Introduction to Data Mining and its Applications, S Sumathi S.N Sivanandam Introduction to Data Mining and its Applications With 108 Figures and 23 Tables 123 Dr S Sumathi Assistant Professor Department of Electrical and Electronics Engineering PSG College of Technology Coimbatore 641 004 Tamil Nadu, India Dr S.N Sivanandam Professor and Head Department of Computer Science and Engineering PSG College of Technology P.O Box 1611 Peelamedu Coimbatore 641 004 Tamil Nadu, India Library of Congress Control Number: 2006926723 ISSN print edition: 1860-949X ISSN electronic edition: 1860-9503 ISBN-10 3-540-34350-4 Springer Berlin Heidelberg New York ISBN-13 978-3-540-34350-9 Springer Berlin Heidelberg New York This work is subject to copyright All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag Violations are liable to prosecution under the German Copyright Law Springer is a part of Springer Science+Business Media springer.com © Springer-Verlag Berlin Heidelberg 2006 The use of general descriptive names, registered names, trademarks, 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 Cover design: deblik, Berlin Typesetting by the authors and SPi Printed on acid-free paper SPIN: 11671213 89/SPi 543210 Contents Introduction to Data Mining Principles 1.1 Data Mining and Knowledge Discovery 1.2 Data Warehousing and Data Mining - Overview 1.2.1 Data Warehousing Overview 1.2.2 Concept of Data Mining 1.3 Summary 20 1.4 Review Questions 20 Data Warehousing, Data Mining, and OLAP 2.1 Data Mining Research Opportunities and Challenges 2.1.1 Recent Research Achievements 2.1.2 Data Mining Application Areas 2.1.3 Success Stories 2.1.4 Trends that Affect Data Mining 2.1.5 Research Challenges 2.1.6 Test Beds and Infrastructure 2.1.7 Findings and Recommendations 2.2 Evolving Data Mining into Solutions for Insights 2.2.1 Trends and Challenges 2.3 Knowledge Extraction Through Data Mining 2.3.1 Data Mining Process 2.3.2 Operational Aspects 2.3.3 The Need and Opportunity for Data Mining 2.3.4 Data Mining Tools and Techniques 2.3.5 Common Applications of Data Mining 2.3.6 What about Data Mining in Power Systems? 2.4 Data Warehousing and OLAP 2.4.1 Data Warehousing for Actuaries 2.4.2 Data Warehouse Components 2.4.3 Management Information 2.4.4 Profit Analysis 21 23 25 27 29 30 31 33 33 35 36 37 39 50 51 52 55 56 57 57 58 59 60 VI Contents 2.5 2.6 2.7 2.4.5 Asset Liability Management Data Mining and OLAP 2.5.1 Research 2.5.2 Data Mining Summary Review Questions 60 61 61 68 72 72 Data Marts and Data Warehouse 75 3.1 Data Marts, Data Warehouse, and OLAP 77 3.1.1 Business Process Re-engineering 77 3.1.2 Real-World Usage 78 3.1.3 Business Intelligence 78 3.1.4 Different Data Structures 82 3.1.5 Different Users 84 3.1.6 Technological Foundation 86 3.1.7 Data Warehouse 87 3.1.8 Informix Architecture 87 3.1.9 Building the Data Warehouse/Data Mart Environment 88 3.1.10 History 91 3.1.11 Nondetailed Data in the Enterprise Data Warehouse 92 3.1.12 Sharing Data Among Data Marts 93 3.1.13 The Manufacturing Process 93 3.1.14 Subdata Marts 95 3.1.15 Refreshment Cycles 95 3.1.16 External Data 96 3.1.17 Operational Data Stores (ODS) and Data Marts 97 3.1.18 Distributed Metadata 98 3.1.19 Managing the Warehouse Environment 100 3.1.20 OLAP 102 3.2 Data Warehousing for Healthcare 107 3.2.1 A Data Warehousing Perspective for Healthcare 107 3.2.2 Adding Value to your Current Data 107 3.2.3 Enhance Customer Relationship Management 108 3.2.4 Improve Provider Management 109 3.2.5 Reduce Fraud 109 3.2.6 Prepare for HEDIS Reporting 110 3.2.7 Disease Management 110 3.2.8 What to Expect When Beginning a Data Warehouse Implementation 110 3.2.9 Definitions 111 3.3 Data Warehousing in the Telecommunications Industry 112 3.3.1 Implementing One View 118 3.3.2 Business Benefit 120 3.3.3 A Holistic Approach 121 Contents 3.4 3.5 3.6 3.7 3.8 VII The Telecommunications Lifecycle 122 3.4.1 Current Enterprise Environment 122 3.4.2 Getting to the Root of the Problem 123 3.4.3 The Telecommunications Lifecycle 125 3.4.4 Telecom Administrative Outsourcing 127 3.4.5 Choose your Outsourcing Partner Wisely 127 3.4.6 Security in Web-Enabled Data Warehouse 128 Security Issues in Data Warehouse 129 3.5.1 Performance vs Security 130 3.5.2 An Ideal Security Model 131 3.5.3 Real-World Implementation 131 3.5.4 Proposed Security Model 136 Data Warehousing: To Buy or To Build a Fundamental Choice for Insurers 140 3.6.1 Executive Overview 140 3.6.2 The Fundamental Choice 140 3.6.3 Analyzing the Strategic Value of Data Warehousing 141 3.6.4 Addressing your Concerns 142 TM 146 3.6.5 Introducing FellowDSS Summary 148 Review Questions 149 Evolution and Scaling of Data Mining Algorithms 151 4.1 Data-Driven Evolution of Data Mining Algorithms 152 4.1.1 Transaction Data 153 4.1.2 Data Streams 154 4.1.3 Graph and Text-Based data 155 4.1.4 Scientific Data 156 4.2 Scaling Mining Algorithms to Large DataBases 157 4.2.1 Prediction Methods 157 4.2.2 Clustering 160 4.2.3 Association Rules 161 4.2.4 From Incremental Model Maintenance to Streaming Data 162 4.3 Summary 163 4.4 Review Questions 164 Emerging Trends and Applications of Data Mining 165 5.1 Emerging Trends in Business Analytics 166 5.1.1 Business Users 166 5.1.2 The Driving Force 167 5.2 Business Applications of Data Mining 170 5.3 Emerging Scientific Applications in Data Mining 177 5.3.1 Biomedical Engineering 177 5.3.2 Telecommunications 178 VIII Contents 5.4 5.5 5.3.3 Geospatial Data 180 5.3.4 Climate Data and the Earth’s Ecosystems 181 Summary 182 Review Questions 183 Data 6.1 6.2 6.3 6.4 6.5 Mining Trends and Knowledge Discovery 185 Getting a Handle on the Problem 186 KDD and Data Mining: Background 187 Related Fields 191 Summary 194 Review Questions 194 Data Mining Tasks, Techniques, and Applications 195 7.1 Reality Check for Data Mining 196 7.1.1 Data Mining Basics 196 7.1.2 The Data Mining Process 197 7.1.3 Data Mining Operations 199 7.1.4 Discovery-Driven Data Mining Techniques: 201 7.2 Data Mining: Tasks, Techniques, and Applications 204 7.2.1 Data Mining Tasks 204 7.2.2 Data Mining Techniques 206 7.2.3 Applications 209 7.2.4 Data Mining Applications – Survey 210 7.3 Summary 215 7.4 Review Questions 216 Data Mining: an Introduction – Case Study 217 8.1 The Data Flood 218 8.2 Data Holds Knowledge 218 8.2.1 Decisions From the Data 219 8.3 Data Mining: A New Approach to Information Overload 219 8.3.1 Finding Patterns in Data, which we can use to Better, Conduct the Business 219 8.3.2 Data Mining can be Breakthrough Technology 220 8.3.3 Data Mining Process in an Information System 221 8.3.4 Characteristics of Data Mining 222 8.3.5 Data Mining Technology 223 8.3.6 Technology Limitations 224 8.3.7 BBC Case Study: The Importance of Business Knowledge 225 8.3.8 Some Medical and Pharmaceutical Applications of Data Mining 228 8.3.9 Why Does Data Mining Work? 228 8.4 Summary 229 8.5 Review Questions 229 Contents IX Data Mining & KDD 231 9.1 Data Mining and KDD – Overview 232 9.1.1 The Idea of Knowledge Discovery in Databases (KDD) 234 9.1.2 How Data Mining Relates to KDD 235 9.1.3 The Data Mining Future 237 9.2 Data Mining: The Two Cultures 238 9.2.1 The Central Issue 238 9.2.2 What are Data Mining and the Data Mining Process?239 9.2.3 Machine Learning 239 9.2.4 Impact of Implementation 240 9.3 Summary 241 9.4 Review Questions 241 10 Statistical Themes and Lessons for Data Mining 243 10.1 Data Mining and Official Statistics 244 10.1.1 What is New in Data Mining is: 244 10.1.2 Goals and Tools of Data Mining 244 10.1.3 New Mines: Texts, Web, Symbolic Data? 245 10.1.4 Applications in Official Statistics 246 10.2 Statistical Themes and Lessons for Data Mining 246 10.2.1 An Overview of Statistical Science 248 10.2.2 Is Data Mining “Statistical Deja Vu” (All Over Again)? 252 10.2.3 Characterizing Uncertainty 254 10.2.4 What Can Go Wrong, Will Go Wrong 256 10.2.5 Symbiosis in Statistics 261 10.3 Summary 262 10.4 Review Questions 263 11 Theoretical Frameworks for Data Mining 265 11.1 Two Simple Approaches 266 11.1.1 Probabilistic Approach 267 11.1.2 Data Compression Approach 268 11.2 Microeconomic View of Data Mining 268 11.3 Inductive Databases 269 11.4 Summary 270 11.5 Review Questions 270 12 Major and Privacy Issues in Data Mining and Knowledge Discovery 271 12.1 Major Issues in Data Mining 272 12.2 Privacy Issues in Knowledge Discovery and Data Mining 275 12.2.1 Revitalized Privacy Threats 277 12.2.2 New Privacy Threats 279 X Contents 12.3 12.4 12.5 12.2.3 Possible Solutions 281 The OECD Personal Privacy Guidelines 283 12.3.1 Risks Privacy and the Principles of Data Protection 284 12.3.2 The OECD Guidelines and Knowledge Discovery 286 12.3.3 Knowledge Discovery about Groups 288 12.3.4 Legal Systems and other Guidelines 289 Summary 290 Review Questions 291 13 Active Data Mining 293 13.1 Shape Definitions 295 13.2 Queries 297 13.3 Triggers 299 13.3.1 Wave Execution Semantics 300 13.4 Summary 302 13.5 Review Questions 302 14 Decomposition in Data Mining - A Case Study 303 14.1 Decomposition in the Literature 304 14.1.1 Machine Learning 304 14.2 Typology of Decomposition in Data Mining 305 14.3 Hybrid Models 306 14.4 Knowledge Structuring 309 14.5 Rule-Structuring Model 310 14.6 Decision Tables, Maps, and Atlases 311 14.7 Summary 312 14.8 Review Questions 313 15 Data 15.1 15.2 15.3 15.4 Mining System Products and Research Prototypes 315 How to Choose a Data Mining System 316 Examples of Commercial Data Mining Systems 318 Summary 319 Review Questions 320 16 Data Mining in Customer Value and Customer Relationship Management 321 16.1 Data Mining: A Concept of Customer Relationship Marketing322 16.1.1 Traditional Marketing Research 322 16.1.2 Relationship Marketing – the Modern View 323 16.1.3 Understanding the Background of Data Mining 324 16.1.4 Continuous Relationship Marketing 326 16.1.5 Developing the Data Mining Project 327 16.1.6 Further Research: 328 16.2 Introduction to Customer Acquisition 328 814 References Samokhvalov, K., (1973) On theory of empirical prediction, (Comp Syst., #55), 3–35 (In Russian) 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DB for Data Mining Specification, Version 1.0 SOAP Version 1.2, http://www.w3.org/TR/soap/ WS-Security, http://www-106.ibm.com/developerworks/webservices/library/ ws-secure/ WS-Resource Framework, http://www.globus.org/wsrf/ XML Specification, http://www.w3.org/TR/2000/REC-xml-20001006 The Data-Mining Industry Coming Of Age Gregory Piatetsky-Shapiro, Knowledge Stream Partnerswww.kdnuggets.com/ gpspubs/ieee-intelligentdec-1999-x6032.pdf Current issues in modeling Data Mining processes and results Panos Xeros [pxeros@cti.gr]& Yannis Theodoridis [ytheod@cti.gr] PANDA informal meeting, Athens,19 June 2002 dke.cti.gr/panda/tasks/meetings/2002–06Athens-informal/CTIpresentation-Athens-19June02.ppt The Physiology of the Grid: An Open Grid Services Architecture for Distributed Systems Integration I Foster, C Kesselman, J Nick, S Tuecke, Open Grid Service Infrastructure WG, Global Grid Forum, June 22, 2002 Java Specification Request 73: Java Data Mining (JDM)–JDM Public review Draft 2003/11/25: JSR-73 Expert Group ... Nets and Timed Automata, 2006 ISBN 3-540-32869-6 Introduction to Data Mining and its Applications, S Sumathi S.N Sivanandam Introduction to Data Mining and its Applications With 108 Figures and. .. preprocessing tools and Data mining tools Introduction to Data Mining Principles Data mining tools are considered for information extraction from data In recent research, data mining through pattern... 2.3.3 The Need and Opportunity for Data Mining 2.3.4 Data Mining Tools and Techniques 2.3.5 Common Applications of Data Mining 2.3.6 What about Data Mining in Power
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