E-Book, Englisch, Band 29, 851 Seiten
Sumathi / Sivanandam Introduction to Data Mining and its Applications
1. Auflage 2006
ISBN: 978-3-540-34351-6
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, Band 29, 851 Seiten
Reihe: Studies in Computational Intelligence
ISBN: 978-3-540-34351-6
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining.
Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization.
This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data.
It is also written for technical managers and executives as well as for technologists interested in learning about data mining.
Autoren/Hrsg.
Weitere Infos & Material
1;Contents;6
2;1 Introduction to Data Mining Principles;24
2.1;1.1 Data Mining and Knowledge Discovery;25
2.2;1.2 Data Warehousing and Data Mining - Overview;28
2.3;1.3 Summary;43
2.4;1.4 Review Questions;43
3;2 Data Warehousing, Data Mining, and OLAP;44
3.1;2.1 Data Mining Research Opportunities and Challenges;46
3.2;2.2 Evolving Data Mining into Solutions for Insights;58
3.3;2.3 Knowledge Extraction Through Data Mining;60
3.4;2.4 Data Warehousing and OLAP;80
3.5;2.5 Data Mining and OLAP;84
3.6;2.6 Summary;95
3.7;2.7 Review Questions;95
4;3 Data Marts and Data Warehouse: Information Architecture for the Millennium;98
4.1;3.1 Data Marts, Data Warehouse, and OLAP;100
4.2;3.2 Data Warehousing for Healthcare: The Greatest Weapon in your Competitive Arsenal;130
4.3;3.3 Data Warehousing in the Telecommunications Industry;135
4.4;3.4 The Telecommunications Lifecycle;145
4.5;3.5 Security Issues in Data Warehouse;152
4.6;3.6 Data Warehousing: To Buy or To Build a Fundamental Choice for Insurers;163
4.7;3.7 Summary;171
4.8;3.8 Review Questions;172
5;4 Evolution and Scaling of Data Mining Algorithms;174
5.1;4.1 Data-Driven Evolution of Data Mining Algorithms;175
5.2;4.2 Scaling Mining Algorithms to Large DataBases;180
5.3;4.3 Summary;186
5.4;4.4 Review Questions;187
6;5 Emerging Trends and Applications of Data Mining;188
6.1;5.1 Emerging Trends in Business Analytics;189
6.2;5.2 Business Applications of Data Mining;193
6.3;5.3 Emerging Scienti.c Applications in Data Mining;200
6.4;5.4 Summary;205
6.5;5.5 Review Questions;206
7;6 Data Mining Trends and Knowledge Discovery;208
7.1;6.1 Getting a Handle on the Problem;209
7.2;6.2 KDD and Data Mining: Background;210
7.3;6.3 Related Fields;214
7.4;6.4 Summary;217
7.5;6.5 Review Questions;217
8;7 Data Mining Tasks, Techniques, and Applications;218
8.1;7.1 Reality Check for Data Mining;219
8.2;7.2 Data Mining: Tasks, Techniques, and Applications;227
8.3;7.3 Summary;238
8.4;7.4 Review Questions;239
9;8 Data Mining: an Introduction – Case Study;240
9.1;8.1 The Data Flood;241
9.2;8.2 Data Holds Knowledge;241
9.3;8.3 Data Mining: A New Approach to Information Overload;242
9.4;8.4 Summary;252
9.5;8.5 Review Questions;252
10;9 Data Mining & KDD;254
10.1;9.1 Data Mining and KDD – Overview;255
10.2;9.2 Data Mining: The Two Cultures;261
10.3;9.3 Summary;264
10.4;9.4 Review Questions;264
11;10 Statistical Themes and Lessons for Data Mining;266
11.1;10.1 Data Mining and O.cial Statistics;267
11.2;10.2 Statistical Themes and Lessons for Data Mining;269
11.3;10.3 Summary;285
11.4;10.4 Review Questions;286
12;11 Theoretical Frameworks for Data Mining;288
12.1;11.1 Two Simple Approaches;289
12.2;11.2 Microeconomic View of Data Mining;291
12.3;11.3 Inductive Databases;292
12.4;11.4 Summary;293
12.5;11.5 Review Questions;293
13;12 Major and Privacy Issues in Data Mining and Knowledge Discovery;294
13.1;12.1 Major Issues in Data Mining;295
13.2;12.2 Privacy Issues in Knowledge Discovery and Data Mining;298
13.3;12.3 Some Privacy Issues in Knowledge Discovery: The OECD Personal Privacy Guidelines;306
13.4;12.4 Summary;313
13.5;12.5 Review Questions;314
14;13 Active Data Mining;316
14.1;13.1 Shape De.nitions;318
14.2;13.2 Queries;320
14.3;13.3 Triggers;322
14.4;13.4 Summary;325
14.5;13.5 Review Questions;325
15;14 Decomposition in Data Mining - A Case Study;326
15.1;14.1 Decomposition in the Literature;327
15.2;14.2 Typology of Decomposition in Data Mining;328
15.3;14.3 Hybrid Models;329
15.4;14.4 Knowledge Structuring;332
15.5;14.5 Rule-Structuring Model;333
15.6;14.6 Decision Tables, Maps, and Atlases;334
15.7;14.7 Summary;335
15.8;14.8 Review Questions;336
16;15 Data Mining System Products and Research Prototypes;338
16.1;15.1 How to Choose a Data Mining System;339
16.2;15.2 Examples of Commercial Data Mining Systems;341
16.3;15.3 Summary;342
16.4;15.4 Review Questions;343
17;16 Data Mining in Customer Value and Customer Relationship Management;344
17.1;16.1 Data Mining: A Concept of Customer Relationship Marketing;345
17.2;16.2 Introduction to Customer Acquisition;351
17.3;16.3 Customer Relationship Management (CRM);358
17.4;16.4 Data Mining and Customer Value and Relationships;371
17.5;16.5 CRM: Technologies and Applications;379
17.6;16.6 Data Management in Analytical Customer Relationship Management;392
17.7;16.7 Summary;408
17.8;16.8 Review Questions;408
18;17 Data Mining in Business;410
18.1;17.1 Business Focus on Data Engineering;411
18.2;17.2 Data Mining for Business Problems;413
18.3;17.3 Data Mining and Business Intelligence;419
18.4;17.4 Data Mining in Business - Case Studies;422
19;18 Data Mining in Sales Marketing and Finance;434
19.1;18.1 Data Mining can Bring Pinpoint Accuracy to Sales;436
19.2;18.2 From Data Mining to Database Marketing;437
19.3;18.3 Data Mining for Marketing Decisions;442
19.4;18.4 Increasing Customer Value by Integrating Data Mining and Campaign Management Software;448
19.5;18.5 Completing a Solution for Market-Basket Analysis – Case Study;454
19.6;18.6 Data Mining in Finance;458
19.7;18.7 Data Mining for Financial Data Analysis;459
19.8;18.8 Summary;460
19.9;18.9 Review Questions;461
20;19 Banking and Commercial Applications;462
20.1;19.1 Bringing Data Mining to the Forefront of Business Intelligence in Wholesale Banking;464
20.2;19.2 Distributed Data Mining Through a Centralized Solution – A Case Study;465
20.3;19.3 Data Mining in Commercial Applications;467
20.4;19.4 Decision Support Systems – Case Study;469
20.5;19.5 Keys to the Commercial Success of Data Mining – Case Studies;475
20.6;19.6 Data Mining Supports E-Commerce;481
20.7;19.7 Data Mining for the Retail Industry;485
20.8;19.8 Business Intelligence and Retailing;486
20.9;19.9 Summary;494
20.10;19.10 Review Questions;495
21;20 Data Mining for Insurance;496
21.1;20.1 Insurance Underwriting: Data Mining as an Underwriting Decision Support Systems;497
21.2;20.2 Business Intelligence and Insurance – Application of Business Intelligence Tools like Data Warehousing, OLAP and Data Mining in Insurance;510
21.3;20.3 Summary;520
21.4;20.4 Review Questions;521
22;21 Data Mining in Biomedicine and Science;522
22.1;21.1 Applications in Medicine;524
22.2;21.2 Data Mining for Biomedical and DNA Data Analysis;525
22.3;21.3 An Unsupervised Neural Network Approach to Medical Data Mining Techniques: Case Study;527
22.4;21.4 Data Mining – Assisted Decision Support for Fever Diagnosis – Case Study;538
22.5;21.5 Data Mining and Science;543
22.6;21.6 Knowledge Discovery in Science as Opposed to Business-Case Study;545
22.7;21.7 Data Mining in a Scienti.c Environment;552
22.8;21.8 Flexible Earth Science Data Mining System Architecture;557
22.9;21.9 Summary;565
22.10;21.10 Review Questions;566
23;22 Text and Web Mining;568
23.1;22.1 Data Mining and the Web;570
23.2;22.2 An Overview on Web Mining;572
23.3;22.3 Text Mining;581
23.4;22.4 Discovering Web Access Patterns and Trends;586
23.5;22.5 Web Usage Mining on Proxy Servers: A Case Study;595
23.6;22.6 Text Data Mining in Biomedical Literature;604
23.7;Approach – Case Study;604
23.8;22.7 Related Work;608
23.9;22.8 Summary;611
23.10;22.9 Review Questions;612
24;23 Data Mining in Information Analysis and Delivery;614
24.1;23.1 Information Analysis: Overview;615
24.2;23.2 Intelligent Information Delivery – Case Study;618
24.3;23.3 A Characterization of Data Mining Technologies and Processes – Case Study;622
24.4;23.4 Summary;635
24.5;23.5 Review Questions;636
25;24 Data Mining in Telecommunications and Control;638
25.1;24.1 Data Mining for the Telecommunication Industry;639
25.2;24.2 Data Mining Focus Areas in Telecommunication;641
25.3;24.3 A Learning System for Decision Support in Telecommunications – Case Study;644
25.4;24.4 Knowledge Processing in Control Systems;646
25.5;24.5 Data Mining for Maintenance of Complex Systems – A Case Study;649
25.6;24.6 Summary;650
25.7;24.7 Review Questions;650
26;25 Data Mining in Security;652
26.1;25.1 Data Mining in Security Systems;653
26.2;25.2 Real Time Data Mining-Based Intrusion Detection Systems – Case Study;654
26.3;25.3 Summary;669
26.4;Review Questions;671
27;APPENDIX-I Data Mining Research Projects;672
27.1;A.1 National University of Singapore: Data Mining Research Projects;672
27.2;A.2 HP Labs Research: Software Technology Laboratory;681
27.3;A.3 CRISP-DM: An Overview;684
27.4;A.4 Data Mining SuiteTM;686
27.5;A.5 The Quest Data Mining System, IBM Almaden Research Center, CA, USA;692
27.6;A.6 The Australian National University Research Projects;699
27.7;A.7 Data Mining Research Group, Monash University Australia;705
27.8;A.8 Current Projects, University of Alabama in Huntsville, AL;711
27.9;A.9 Kensington Approach Toward Enterprise Data Mining;719
28;APPENDIX-II Data Mining Standards;722
28.1;II.1 Data Mining Standards;723
28.2;II.2 Developing Data Mining Application Using Data Mining Standards;742
28.3;II.3 Analysis;745
28.4;II.4 Application Examples;746
28.5;II.5 Conclusion;753
29;Appendix 3A Intelligent Miner;754
29.1;3A.1 Data Mining Process;754
29.2;3A.2 Interpreting the Results;756
29.3;3A.3 Overview of the Intelligent Miner Components;757
29.4;3A.4 Running Intelligent Miner Servers;757
29.5;3A.5 How the Intelligent Miner Creates Output Data;759
29.6;3A.6 Performing Common Tasks;760
29.7;3A.7 Understanding Basic Concepts;761
29.8;3A.8 Main Window Areas;761
29.9;3A.9 Conclusion;763
30;Appendix 3B Clementine;764
30.1;3B.1 Key Findings;764
30.2;3B.2 Background Information;765
30.3;3B.3 Product Availability;766
30.4;3B.4 Software Description;767
30.5;3B.5 Architecture;768
30.6;3B.6 Methodology;769
30.7;3B.7 Clementine Server;776
30.8;3B.8 How Clementine Server Improves Performance on Large Datasets;777
30.9;3B.9 Conclusion;781
31;Appendix 3C Crisp;784
31.1;3C.1 Hierarchical Breakdown;784
31.2;3C.2 Mapping Generic Models to Specialized Models;785
31.3;3C.3 The CRISP-DM Reference Model;786
31.4;3C.4 Data Understanding;792
31.5;3C.5 Data Preparation;794
31.6;3C.6 Modeling;797
31.7;3C.7 Evaluation;799
31.8;3C.8 Conclusion;800
32;Appendix 3D Mineset;802
32.1;3D.1 Introduction;802
32.2;3D.2 Architecture;802
32.3;3D.3 MineSet Tools for Data Mining Tasks;803
32.4;3D.4 About the Raw Data;804
32.5;3D.5 Analytical Algorithms;804
32.6;3D.6 Visualization;805
32.7;3D.7 KDD Process Management;806
32.8;3D.8 History;807
32.9;3D.9 Commercial Uses;808
32.10;3D.10 Conclusion;809
33;Appendix 3E Enterprise Miner;810
33.1;3E.1 Tools For Data Mining Process;810
33.2;3E.2 Why Enterprise Miner;811
33.3;3E.3 Product Overview;812
33.4;3E.4 SAS Enterprise Miner 5.2 Key Features;813
33.5;3E.5 Enterprise Miner Software;816
33.6;3E.6 Enterprise Miner Process for Data Mining;819
33.7;3E.7 Client/Server Capabilities;819
33.8;3E.8 Client/Server Requirements;819
33.9;3E.9 Conclusion;820
34;References;822
2 Data Warehousing, Data Mining, and OLAP (p. 21)
Objectives:
• This deals with the concept of data mining, need and opportunities, trends and challenges, data mining process, common and new applications of data mining, data warehousing, and OLAP concepts.
• It gives an introduction to data mining: what it is, why it is important, and how it can be used to provide increased understanding of critical relationships in rapidly expanding corporate data warehouse.
• Data mining and knowledge discovery are emerging as a new discipline with important applications in science, engineering, health care, education, and business.
• New disciplined approaches to data warehousing and mining are emerging as part of the vertical solutions approach.
• Extracting the information and knowledge in the form of new relationships, patterns, or clusters for decision making purposes.
• We briefly describe some success stories involving data mining and knowledge discovery.
• We describe five external trends that promise to have a fundamental impact on data mining.
• The research challenges are divided into five broad areas: A) improving the scalability of data mining algorithms, B) mining nonvector data, C) mining distributed data, D) improving the ease of use of the data mining systems and environments, and E) privacy and security issues for data mining.
• We present the concept of data mining and aim at providing an understanding of the overall process and tools involved: how the process turns out, what can be done with it, what are the main techniques behind it, and which are the operational aspects.
• OLAP servers logically organize data in multiple dimensions, which allows users to quickly and easily analyze complex data relationships.
• OLAP database servers support common analytical operations, including consolidation, drill-down, and slicing and dicing.
• OLAP servers are very eficient when storing and processing multidimensional data.
Abstract.
This deals with the concept of data mining, need and opportunities, trends and challenges, process, common and new applications, data warehousing, and OLAP concepts. Data mining is also a promising computational paradigm that enhances traditional approaches to discovery and increases the opportunities for breakthroughs in the understanding of complex physical and biological systems.
Researchers from many intellectual communities have much to contribute to this field. Data mining refers to the act of extracting patterns or models from data. The rate growth of disk storage and the gap between Moore’s law and storage law growth trends represent a very interesting pattern in the state of technology evolution. The ability to capture and store data has produced a phenomenon we call the data tombs or data stores that are effectively write-only.
"Data Mining" (DM) is a folkloric denomination of a complex activity, which aims at extracting synthesized and previously unknown information from large databases. It also denotes a multidisciplinary field of research and development of algorithms and software environments to support this activity in the context of real-life problems where often huge amounts of data are available for mining.
