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1.

図書
図書
1. Data mining : concepts, models, methods, and algorithms
Mehmed Kantardzic ; IEEE Computer Society, sponser
出版情報: Piscataway, NJ : IEEE Press , Hoboken, NJ : Wiley-Interscience, c2003  xii, 345 p. ; 26 cm
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2.

図書
図書
2. Information-statistical data mining : warehouse integration with examples of Oracle basics
by Bon K. Sy, Arjun K. Gupta
出版情報: Boston, Mass. : Kluwer Academic, c2004  xxii, 289 p. ; 24 cm
シリーズ名: The Kluwer international series in engineering and computer science ; SECS 757
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目次情報: 続きを見る
Inspiration
Dedication
Contributing Authors and Contact Information
Preface
Acknowledgments
Preview: Data Warehousing/Mining / 1：
What Is Summary Information?
Data, Information Theory, Statistics / 2：
Data Warehousing/Mining Management / 3：
Architecture, Tools And Applications / 4：
Conceptual/Practical Mining Tools / 5：
Conclusion / 6：
Data Warehouse Basics
Methodology
Concept of Patterns & Visualization
Introduction
Appendix: Word problem solution
Information Theory & Statistics
Information theory
Variable interdependence measure
Probability model comparison
Pearson's Chi-Square statistic
Information and Statistics Linkage
Statistics
Concept of information
Information theory and statistics
Temporal-Spatial Data
Temporal-spatial characteristics
Temporal-spatial data analysis
Problem formulation
Temperature analysis application
Discussion
Change Point Detection Techniques / 7：
Change point problem
Information criterion approach
Binary segmentation technique
Example
Statistical Association Patterns / 8：
Information-Statistical Association
Pattern Inference & Model Discovery / 9：
Concept of pattern-based inference
Conclusion. Appendix: Pattern utility illustration
Bayesian Nets & Model Generation / 0：
Preliminary of Bayesian Networks
Pattern Synthesis for MODEL Learning
Pattern Ordering Inference: Part I / 11：
Pattern Ordering Inference: Part II / 12：
Ordering General Event Patterns
51 largest PR(ADHJ BCE F &Gmacr; &Imacu;) / Appendix I：
ordering Of PR(LúY/Yú SE). SE=F G I / Appendix II：
Evaluation of Method A. Appendix III / Appendix III：
Evaluation of Method B. Appendix III / B：
Evaluation of Method C / C：
Oracle Data Warehouse / 13：
Background
Challenge
Illustrations
Warehouse Data Dictionary
Financial Data Analysis / 14：
The data
Information theoretic approach
data analysis
Forest Classification / 15：
Classifier model derivation
Test data characteristics
Experimental platform
Classification results
Validation stage
Effect of mixed data on performance
Goodness measure for evaluation
Conclusion. References. Index. Web resource: http://www.techsuite.net/kluwer/
Web Accessible Scientific Data Warehouse Example
MathCAD Implementation of Change Point Detection
S-PLUS open source code for Statistical Association
Internet Downloadable Model Discovery Tool
Software Tool for Singly Connected Bayesian Model.
Inspiration
Dedication
Contributing Authors and Contact Information
3.

図書
図書
3. Knowledge discovery and measures of interest
by Robert J. Hilderman, Howard J. Hamilton
出版情報: Boston, MA : Kluwer Academic Publishers, c2001  xvii, 162 p. ; 25 cm
シリーズ名: The Kluwer international series in engineering and computer science ; SECS 638
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目次情報: 続きを見る
List of Figures
List of Tables
Preface
Acknowledgments
Introduction / 1.：
KDD in a Nutshell / 1.1：
The Mining Step / 1.1.1：
The Interpretation and Evaluation Step / 1.1.2：
Objective of the Book / 1.2：
Background and Related Work / 2.：
Data Mining Techniques / 2.1：
Classification / 2.1.1：
Association / 2.1.2：
Clustering / 2.1.3：
Correlation / 2.1.4：
Other Techniques / 2.1.5：
Interestingness Measures / 2.2：
Rule Interest Function / 2.2.1：
J-Measure / 2.2.2：
Itemset Measures / 2.2.3：
Rule Templates / 2.2.4：
Projected Savings / 2.2.5：
I-Measures / 2.2.6：
Silbershatz and Tuzhilin's Interestingness / 2.2.7：
Kamber and Shinghal's Interestingness / 2.2.8：
Credibility / 2.2.9：
General Impressions / 2.2.10：
Distance Metric / 2.2.11：
Surprisingness / 2.2.12：
Gray and Orlowska's Interestingness / 2.2.13：
Dong and Li's Interestingness / 2.2.14：
Reliable Exceptions / 2.2.15：
Peculiarity / 2.2.16：
A Data Mining Technique / 3.：
Definitions / 3.1：
The Serial Algorithm / 3.2：
General Overview / 3.2.1：
Detailed Walkthrough / 3.2.2：
The Parallel Algorithm / 3.3：
Complexity Analysis / 3.3.1：
Attribute-Oriented Generalization / 3.4.1：
The All_Gen Algorithm / 3.4.2：
A Comparison with Commercial OLAP Systems / 3.5：
Heuristic Measures of Interestingness / 4.：
Diversity / 4.1：
Notation / 4.2：
The Sixteen Diversity Measures / 4.3：
The I[subscript Variance] Measure / 4.3.1：
The I[subscript Simpson] Measure / 4.3.2：
The I[subscript Shannon] Measure / 4.3.3：
The I[subscript Total] Measure / 4.3.4：
The I[subscript Max] Measure / 4.3.5：
The I[subscript McIntosh] Measure / 4.3.6：
The I[subscript Lorenz] Measure / 4.3.7：
The I[subscript Gini] Measure / 4.3.8：
The I[subscript Berger] Measure / 4.3.9：
The I[subscript Schutz] Measure / 4.3.10：
The I[subscript Bray] Measure / 4.3.11：
The I[subscript Whittaker] Measure / 4.3.12：
The I[subscript Kullback] Measure / 4.3.13：
The I[subscript MacArthur] Measure / 4.3.14：
The I[subscript Theil] Measure / 4.3.15：
The I[subscript Atkinson] Measure / 4.3.16：
An Interestingness Framework / 5.：
Interestingness Principles / 5.1：
Summary / 5.2：
Theorems and Proofs / 5.3：
Minimum Value Principle / 5.3.1：
Maximum Value Principle / 5.3.2：
Skewness Principle / 5.3.3：
Permutation Invariance Principle / 5.3.4：
Transfer Principle / 5.3.5：
Experimental Analyses / 6.：
Evaluation of the All_Gen Algorithm / 6.1：
Serial vs Parallel Performance / 6.1.1：
Speedup and Efficiency Improvements / 6.1.2：
Evaluation of the Sixteen Diversity Measures / 6.2：
Comparison of Assigned Ranks / 6.2.1：
Analysis of Ranking Similarities / 6.2.2：
Analysis of Summary Complexity / 6.2.3：
Distribution of Index Values / 6.2.4：
Conclusion / 7.：
Areas for Future Research / 7.1：
Appendices
Ranking Similarities
Summary Complexity
Index
List of Figures
List of Tables
Preface
4.

図書
図書
4. Data mining with Microsoft SQL server 2008
Jamie MacLennan, ZhaoHui Tang, Bogdan Crivat
出版情報: Indianapolis, IN : Wiley Pub., c2009  xxxvi, 636 p. ; 24 cm
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Introduction to Data Mining / 1：
Applied Data Mining Using Microsoft Excel 2007 / 2：
DMX and SQL Server Data Mining Concepts / 3：
Using SQL Server Data Mining / 4：
Implementing a Data Mining Process Using Office 2007 / 5：
Microsoft Naïve Bayes / 6：
Microsoft Decision Trees Algorithm / 7：
Microsoft Time Series Algorithm / 8：
Microsoft Clustering / 9：
Microsoft Sequence Clustering / 10：
Microsoft Association Rules / 11：
Microsoft Neural Network and Logistic Regression / 12：
Mining OLAP Cubes / 13：
Data Mining with SQL Server Integration Services / 14：
SQL Server Data Mining Architecture / 15：
Programming SQL Server Data Mining / 16：
Extending SQL Server Data Mining / 17：
Implementing a Web Cross-Selling Application / 18：
Conclusion and Additional Resources / 19：
Datasets / Appendix A：
Supported Functions / Appendix B：
Index
Introduction to Data Mining / 1：
Applied Data Mining Using Microsoft Excel 2007 / 2：
DMX and SQL Server Data Mining Concepts / 3：
5.

図書
図書
5. Data mining : concepts and techniques
Jiawei Han, Micheline Kamber
出版情報: San Francisco ; Tokyo : Morgan Kaufmann Publishers, c2001  xxiv, 550 p. ; 24 cm
シリーズ名: The Morgan Kaufmann series in data management systems
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Introduction / 1：
Data Warehouse and OLAP Technology for Data Mining / 2：
Data Preparation / 3：
Data Mining Primitives, Languages, and System Architectures / 4：
Concept Description: Characterization and Comparison / 5：
Mining Association Rules in Large Databases / 6：
Classification and Prediction / 7：
Cluster Analysis / 8：
Mining Complex Types of Data / 9：
Data Mining Applications and Trends in Data Mining / 10：
An Introduction to Microsoft's OLE DB for Data Mining / Appendix A：
An Introduction to DBMiner / Appendix B：
Bibliography
Introduction / 1：
Data Warehouse and OLAP Technology for Data Mining / 2：
Data Preparation / 3：
6.

図書
図書
6. Data mining for association rules and sequential patterns : sequential and parallel algorithms
Jean-Marc Adamo
出版情報: New York : Springer, c2001  x, 254 p. ; 25 cm
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7.

図書
図書
7. Relational data mining
Sašo Džeroski, Nada Lavrač (eds.)
出版情報: Berlin : Springer, c2001  xix, 398 p. ; 24 cm
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8.

図書
図書
8. Data mining and uncertain reasoning : an integrated approach
Zhengxin Chen
出版情報: New York : Jonh Wiley & Sons, c2001  xiv, 370 p. ; 25 cm
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What This Book Is About
Basics of Data Mining
Enabling Techniques and Advanced Features of Data Mining
Dealing with Uncertainty in Manipulation of Data
Data Mining Tasks for Knowledge Discovery
Bayesian Networks and Artificial Neural Networks
Uncertain Reasoning Techniques for Data Mining
Data Mining Lifecycle with Uncertainty Handling: Case Studies and Software Tools
Intelligent Conceptual Query Answering with Uncertainty: Basic Aspects and Case Studies
References
Index
What This Book Is About
Basics of Data Mining
Enabling Techniques and Advanced Features of Data Mining
9.

図書
図書
9. Graphical models : methods for data analysis and mining
Christian Borgelt and Rudolf Kruse
出版情報: Chichester : J. Wiley, c2002  viii, 358 p. ; 24 cm
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Preface
Introduction / 1：
Data and Knowledge / 1.1：
Knowledge Discovery and Data Mining / 1.2：
The KDD Process / 1.2.1：
Data Mining Tasks / 1.2.2：
Data Mining Methods / 1.2.3：
Graphical Models / 1.3：
Outline of this Book / 1.4：
Imprecision and Uncertainty / 2：
Modeling Inferences / 2.1：
Imprecision and Relational Algebra / 2.2：
Uncertainty and Probability Theory / 2.3：
Possibility Theory and the Context Model / 2.4：
Experiments with Dice / 2.4.1：
The Context Model / 2.4.2：
The Insufficient Reason Principle / 2.4.3：
Overlapping Contexts / 2.4.4：
Mathematical Formalization / 2.4.5：
Normalization and Consistency / 2.4.6：
Possibility Measures / 2.4.7：
Mass Assignment Theory / 2.4.8：
Degrees of Possibility for Decision Making / 2.4.9：
Conditional Degrees of Possibility / 2.4.10：
Open Problems / 2.4.11：
Decomposition / 3：
Decomposition and Reasoning / 3.1：
Relational Decomposition / 3.2：
A Simple Example / 3.2.1：
Reasoning in the Simple Example / 3.2.2：
Decomposability of Relations / 3.2.3：
Tuple-Based Formalization / 3.2.4：
Possibility-Based Formalization / 3.2.5：
Conditional Possibility and Independence / 3.2.6：
Probabilistic Decomposition / 3.3：
Factorization of Probability Distributions / 3.3.1：
Conditional Probability and Independence / 3.3.4：
Possibilistic Decomposition / 3.4：
Transfer from Relational Decomposition / 3.4.1：
Conditional Degrees of Possibility and Independence / 3.4.2：
Possibility versus Probability / 3.5：
Graphical Representation / 4：
Conditional Independence Graphs / 4.1：
Axioms of Conditional Independence / 4.1.1：
Graph Terminology / 4.1.2：
Separation in Graphs / 4.1.3：
Dependence and Independence Maps / 4.1.4：
Markov Properties of Graphs / 4.1.5：
Graphs and Decompositions / 4.1.6：
Markov Networks and Bayesian Networks / 4.1.7：
Evidence Propagation in Graphs / 4.2：
Propagation in Polytrees / 4.2.1：
Join Tree Propagation / 4.2.2：
Other Evidence Propagation Methods / 4.2.3：
Computing Projections / 5：
Databases of Sample Cases / 5.1：
Relational and Sum Projections / 5.2：
Expectation Maximization / 5.3：
Maximum Projections / 5.4：
Computation via the Support / 5.4.1：
Computation via the Closure / 5.4.3：
Experimental Results / 5.4.4：
Limitations / 5.4.5：
Naive Classifiers / 6：
Naive Bayes Classifiers / 6.1：
The Basic Formula / 6.1.1：
Relation to Bayesian Networks / 6.1.2：
A Naive Possibilistic Classifier / 6.1.3：
Classifier Simplification / 6.3：
Learning Global Structure / 6.4：
Principles of Learning Global Structure / 7.1：
Learning Relational Networks / 7.1.1：
Learning Probabilistic Networks / 7.1.2：
Learning Possibilistic Networks / 7.1.3：
Components of a Learning Algorithm / 7.1.4：
Evaluation Measures / 7.2：
General Considerations / 7.2.1：
Notation and Presuppositions / 7.2.2：
Relational Evaluation Measures / 7.2.3：
Probabilistic Evaluation Measures / 7.2.4：
Possibilistic Evaluation Measures / 7.2.5：
Search Methods / 7.3：
Exhaustive Graph Search / 7.3.1：
Guided Random Graph Search / 7.3.2：
Conditional Independence Search / 7.3.3：
Greedy Search / 7.3.4：
Learning Local Structure / 7.4：
Local Network Structure / 8.1：
Inductive Causation / 8.2：
Correlation and Causation / 9.1：
Causal and Probabilistic Structure / 9.2：
Stability and Latent Variables / 9.3：
The Inductive Causation Algorithm / 9.4：
Critique of the Underlying Assumptions / 9.5：
Evaluation / 9.6：
Applications / 10：
Application in Telecommunications / 10.1：
Application at Volkswagen / 10.2：
Application at Daimler Chrysler / 10.3：
Proofs of Theorems / A：
Proof of Theorem 4.1.2 / A.1：
Proof of Theorem 4.1.18 / A.2：
Proof of Theorem 4.1.20 / A.3：
Proof of Theorem 4.1.22 / A.4：
Proof of Theorem 4.1.24 / A.5：
Proof of Theorem 4.1.26 / A.6：
Proof of Theorem 4.1.27 / A.7：
Proof of Theorem 5.4.8 / A.8：
Proof of Theorem 7.3.2 / A.9：
Proof of Lemma 7.2.2 / A.10：
Proof of Lemma 7.2.4 / A.11：
Proof of Lemma 7.2.6 / A.12：
Proof of Theorem 7.3.4 / A.13：
Proof of Theorem 7.3.5 / A.14：
Proof of Theorem 7.3.6 / A.15：
Proof of Theorem 7.3.8 / A.16：
Software Tools / B：
Bibliography
Index
Preface
Introduction / 1：
Data and Knowledge / 1.1：
10.

図書
図書
10. Data mining and knowledge discovery with evolutionary algorithms
Alex A. Freitas
出版情報: Berlin : Springer, c2002  xiv, 264 p. ; 24 cm
シリーズ名: Natural computing series
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