Data Mining and Knowledge Discovery Handbook

Oded Maimon, Lior Rokach

Springer Science & Business Media, 2006 M05 28 - 1383 pages

Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository.

This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security.

Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.

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Selected pages

Acknowledgments	88

Discretization Methods	113

39	130

8	148

The Curse of Dimensionality	159

Classification Problem Extensions	161

References	162

Decision Trees	165

Web Mining	899

Graph Properties of the Web	900

Web Search	902

Text Classification	904

Hypertext Classification	905

Information Extraction and Wrapper Induction	907

The Semantic Web	908

Web Usage Mining	909

Algorithmic Framework for Decision Trees	167

Univariate Splitting Criteria	168

Multivariate Splitting Criteria	174

Pruning Methods	175

Other Issues	179

Decision Trees Inducers	181

Advantages and Disadvantages of Decision Trees	183

Decision Tree Extensions	185

References	187

Bayesian Networks	193

Representation	195

Reasoning	198

Learning	200

Bayesian Networks in Data Mining	211

Data Mining Applications	218

Conclusions and Future Research Directions	223

Acknowledgments	226

Data Mining within a Regression Framework	231

Some Definitions	232

Regression Splines	234

Smoothing Splines	236

Locally Weighted Regression as a Smoother	238

Smoothers for Multiple Predictors	239

Recursive Partitioning	242

Conclusions	252

References	253

Rule Induction	277

14	296

48	314

15	321

16	351

Application to Other Types of Data	362

Extensions of the Basic Framework	364

Conclusions	372

References	373

Frequent Set Mining	377

Problem Description	378

Apriori	381

Eclat	384

Optimizations	386

Concise representations	388

Theoretical Aspects	391

Further Reading	392

References	393

18	395

Constraintbased Data Mining 399	398

Background and Notations	402

Solving AntiMonotonic Constraints	404

Introducing non AntiMonotonic Constraints	406

Conclusion	413

References	414

Link Analysis	417

Social Network Analysis	419

Search Engines	422

Viral Marketing	424

Law Enforcement Fraud Detection	426

Combining with Traditional Methods	428

Summary	430

Soft Computing Methods	433

Evolutionary Algorithms for Data Mining	435

An Overview of Evolutionary Algorithms	436

Evolutionary Algorithms for Discovering Classification Rules	442

Evolutionary Algorithms for Clustering	447

Evolutionary Algorithms for Data Preprocessing	450

MultiObjective Optimization with Evolutionary Algorithms	456

Conclusions	459

References	461

an Overview from a Data Mining Perspective 469	468

The ReinforcementLearning Model	470

ReinforcementLearning Algorithms	472

Extensions to Basic Model and Algorithms	476

Applications of ReinforcementLearning	478

ReinforcementLearning and DataMining	479

An Instructive Example	480

References	485

Neural Networks	487

A Brief History	488

Neural Network Models	490

Data Mining Applications	506

Conclusions	508

23	510

On the use of Fuzzy Logic in Data Mining	517

Fuzzy Sets and Fuzzy Logic	518

Soft Regression	522

Fuzzy Association Rules	525

Conclusions	532

Granular Computing and Rough Sets	535

Naive Model for Problem Solving	536

A Geometric Models of Information Granulations	538

Information GranulationsPartitions	540

Nonpartition Application Chinese Wall Security Policy Model	541

Knowledge Representations	543

Topological Concept Hierarchy LatticesTrees	549

Knowledge Processing	553

Information Integration	556

Conclusions	558

Supporting Methods	562

Statistical Methods for Data Mining	565

Statistical Issues in DM	567

Modeling Relationships using Regression Models	573

False Discovery Rate FDR Control in Hypotheses Testing	578

Model Variables or Features Selection using FDR Penalization in GLM	582

Concluding Remarks	584

References	585

Logics for Data Mining 589	588

Generalized quantifiers	590

Some important classes of quantifiers	593

Some comments and conclusion	598

Acknowledgments	599

Wavelet Methods in Data Mining	603

Tao Li Sheng Ma and Mitsunori Ogihara 1 Introduction	604

Wavelet Background	605

Data Management	610

Preprocessing	611

Core Mining Process	614

Conclusion	622

References	623

Fractal Mining	627

Daniel Barbara and Ping Chen 1 Introduction	628

Fractal Dimension	629

Clustering Using the Fractal Dimension	633

Projected Fractal Clustering	641

Tracking Clusters	642

Conclusions	645

29	649

30	661

31	695

32	715

33	731

34	749

35	764

Haixun Wang Philip S Yu and Jiawei	778

37	793

Formal Frameworks And AlgorithmBased Techniques	808

Hybrid Approaches TEG	814

Text Mining Visualization and Analytics	815

References	820

39	828

Spatial Data Mining 833	832

Spatial Data	834

Spatial Outliers	837

Spatial Colocation Rules	841

Predictive Models	844

Spatial Clusters	848

Summary	849

References	850

An Overview	853

Performance Measure	854

Sampling Strategies	858

Ensemblebased Methods	860

Discussion	862

References	863

Relational Data Mining 869	868

Inductive logic programming	874

Relational Association Rules	884

Relational Decision Trees	889

RDM Literature and Internet Resources	894

References	895

Collaborative Filtering	910

Conclusion	911

43	912

A Review of Web Document Clustering Approaches	921

Nora Oikonomakou and Michalis Vazirgiannis 1 Introduction	922

Web Document Clustering Approaches	924

Comparison	935

Conclusions and Open Issues	937

Causal Discovery	945

Background Knowledge	946

Theoretical Foundation	949

Learning a DAG of CN by FDs	950

Experimental Results	953

References	954

Ensemble Methods For Classifiers 957	956

Sequential Methodology	958

Concurrent Methodology	964

Combining Classifiers	966

Ensemble Diversity	973

Ensemble Size	974

Cluster Ensemble	976

References	977

Decomposition Methodology for	981

Decomposition Advantages	984

The Elementary Decomposition Methodology	986

The Decomposers Characteristics	991

The Relation to Other Methodologies	996

Summary	999

Information Fusion	1005

Preprocessing Data	1006

Building Data Models	1009

Information Extraction	1012

References	1013

Parallel And GridBased Data Mining	1017

Antonio Congiusta Domenico Talia and Paolo Trunfio 1 Introduction	1018

Parallel Data Mining	1019

GridBased Data Mining	1027

The Knowledge Grid	1033

Summary	1038

References	1039

Collaborative Data Mining 1043	1042

Remote Collaboration	1044

The Data Mining Process	1047

Collaborative Data Mining Guidelines	1048

Discussion	1052

Conclusions	1053

References	1054

50	1056

Organizational Data Mining	1057

Hamid R Nemati and Christopher D Barko 1 Introduction	1058

Organizational Data Mining	1059

ODM versus Data Mining	1060

Ongoing ODM Research	1062

ODM Evolution	1063

Summary	1066

Mining Time Series Data	1069

Time Series Similarity Measures	1071

Time Series Data Mining	1077

Time Series Representations	1088

Summary	1098

Applications	1104

Data Mining in Medicine	1107

Symbolic Classification Methods	1109

Subsymbolic Classification Methods	1120

Other Methods Supporting Medical Knowledge Discovery	1126

Conclusions	1129

53	1130

Learning Information Patterns in	1139

Learning Stochastic Pattern Models	1141

Searching for MetaPatterns	1148

Conclusions	1156

Data Mining for Selection of Manufacturing Processes	1159

Data Mining in Engineering	1160

Selection of Manufacturing Process with a Data Mining Approach	1161

Conclusion	1165

References	1166

Data Mining of Design Products and Processes	1167

Product Design Process	1169

Product Portfolio Management	1171

Conceptual Design	1172

Detailed Design	1175

Business and Manufacturing Process Planning	1177

Text Mining	1178

Observations and Future Advancements	1180

Epilogue	1182

References	1183

Data Mining in Telecommunications 1189	1188

Types of Telecommunication Data	1190

Data Mining Applications	1194

Conclusion	1199

References	1200

Data Mining for Financial Applications	1203

Specifics of Data Mining in Finance	1205

Aspects of Data Mining Methodology in Finance	1210

Data Mining Models and Practice in Finance	1214

Conclusion	1219

References	1221

58	1223

Data Mining for Intrusion Detection	1225

Data Mining Basics	1226

Data Mining Meets Intrusion Detection	1228

Conclusions and Future Research Directions	1235

References	1236

Data Mining For Software Testing 1239	1238

Mining Software Metrics Databases	1241

InteractionPattern Discovery in System Usage Data	1242

Using Data Mining in Functional Testing	1243

Summary	1246

Acknowledgments	1247

Data Mining for CRM	1249

Data Mining and Campaign Management	1251

Customer Acquisition	1252

Data Mining for Target Marketing	1261

Modeling Process	1263

Evaluation Metrics	1265

Segmentation Methods	1268

Predictive Modeling	1275

InMarket Timing	1281

Pitfalls of Targeting	1285

Conclusions	1297

References	1299

Software	1302

Weka	1305

References	1313

Oracle Data Mining	1315

The MiningintheDatabase Paradigm	1317

ODM Functionality and Algorithms	1319

Text and Spatial Mining	1324

ODM Examples	1325

Conclusions	1327

References	1328

Building Data Mining Solutions with 1331	1330

OLE DB for Data Mining	1332

Data Mining in SQL Server 2000	1336

Building Data Mining Application using OLE DB for Data Mining	1338

for Analysis	1340

Conclusion	1343

References	1344

LERSA Data Mining System	1347

Input Data	1348

Main Features	1349

Final Remarks	1350

GainSmarts Data Mining System 1353	1352

Accessing GainSmarts	1354

Setting Up the Data for Modeling	1355

Knowledge Evaluation	1360

Software Characteristics	1363

WizSofts WizWhy	1365

IfThen Rules	1366

Data Summarization	1367

Classifications	1368

WizWhy vs other Data Mining Methods	1369

DataEngine	1371

Intelligent Technologies for Modeling and Control	1372

Work with DataEngine	1374

References	1377

Index 1379	1378

Copyright

Other editions - View all

Data Mining and Knowledge Discovery Handbook
Oded Z. Maimon,Oded Maimon,Lior Rokach
Limited preview - 2005

Data Mining and Knowledge Discovery Handbook
Oded Z. Maimon,Oded Maimon,Lior Rokach
No preview available - 2005

Common terms and phrases

accuracy analysis applications approach Artificial Intelligence association rules Bayesian networks bias binary classifier complexity compute concept constraints criteria cross-validation data cleansing Data Mining data set database decision tree defined denotes dimension dimensional directed acyclic graph Discovery and Data discretization distance distribution domain Equation error estimate evaluation example feature selection Figure frequent sets function fuzzy given global Headache IEEE input instance interestingness International Conference itemsets kernel Knowledge Discovery linear Machine Learning marginal likelihood Markov property matrix measure meta-learning methods minimal missing attribute values multivariate neural networks node optimal outlier detection parameters partition patterns performance points prediction predictor probability problem Proc procedure Proceedings pruning query regression representation represented Rough Sets rule induction sample space split statistical subset supervised learning support vector support vector machines Systems Table techniques Temperature tion training set variables variance wavelet Y₁ yes yes

Bibliographic information

Title	Data Mining and Knowledge Discovery Handbook
Editors	Oded Maimon, Lior Rokach
Edition	illustrated
Publisher	Springer Science & Business Media, 2006
ISBN	038725465X, 9780387254654
Length	1383 pages

Export Citation	BiBTeX EndNote RefMan

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