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Data Mining : Special Issue in Annals of Information Systems / edited by Robert Stahlbock, Sven F. Crone, Stefan Lessmann
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Annals of Information Systems ; 8 | SpringerLink BücherPublisher: Boston, MA : Springer Science+Business Media, LLC, 2010Edition: 1Description: Online-Ressource (XIII, 387 p, online resource)ISBN:- 9781441912800
- 006.312
- QA76.9.D343
Contents:
Summary: Over the course of the last twenty years, research in data mining has seen a substantial increase in interest, attracting original contributions from various disciplines including computer science, statistics, operations research, and information systems. Data mining supports a wide range of applications, from medical decision making, bioinformatics, web-usage mining, and text and image recognition to prominent business applications in corporate planning, direct marketing, and credit scoring. Research in information systems equally reflects this inter- and multidisciplinary approach, thereby aPPN: PPN: 1649905874Package identifier: Produktsigel: ZDB-2-SCS
Preface; Contents; 1 Data Mining and Information Systems: Quo Vadis?; Robert Stahlbock, Stefan Lessmann, and Sven F. Crone; 1.1 Introduction; 1.2 Special Issues in Data Mining; 1.2.1 Confirmatory Data Analysis; 1.2.2 Knowledge Discovery from Supervised Learning; 1.2.3 Classification Analysis; 1.2.4 Hybrid Data Mining Procedures; 1.2.5 Web Mining; 1.2.6 Privacy-Preserving Data Mining; 1.3 Conclusion and Outlook; References; Part I Confirmatory Data Analysis; 2 Response-Based Segmentation Using Finite Mixture Partial Least Squares; Christian M. Ringle, Marko Sarstedt, and Erik A. Mooi
2.1 Introduction2.1.1 On the Use of PLS Path Modeling; 2.1.2 Problem Statement; 2.1.3 Objectives and Organization; 2.2 Partial Least Squares Path Modeling; 2.3 Finite Mixture Partial Least Squares Segmentation; 2.3.1 Foundations; 2.3.2 Methodology; 2.3.3 Systematic Application of FIMIX-PLS; 2.4 Application of FIMIX-PLS; 2.4.1 On Measuring Customer Satisfaction; 2.4.2 Data and Measures; 2.4.3 Data Analysis and Results; 2.5 Summary and Conclusion; References; Part II Knowledge Discovery from Supervised Learning; 3 Building Acceptable Classification Models; David Martens and Bart Baesens
3.1 Introduction3.2 Comprehensibility of Classification Models; 3.2.1 Measuring Comprehensibility; 3.2.2 Obtaining Comprehensible Classification Models; 3.2.2.1 Building Rule-Based Models; 3.2.2.2 Combining Output Types; 3.2.2.3 Visualization; 3.3 Justifiability of Classification Models; 3.3.1 Taxonomy of Constraints; 3.3.2 Monotonicity Constraint; 3.3.3 Measuring Justifiability; 3.3.4 Obtaining Justifiable Classification Models; 3.4 Conclusion; References; 4 Mining Interesting Rules Without Support Requirement: A General Universal Existential Upward Closure Property
Yannick Le Bras, Philippe Lenca, and Stéphane Lallich4.1 Introduction; 4.2 State of the Art; 4.3 An Algorithmic Property of Confidence; 4.3.1 On UEUC Framework; 4.3.2 The UEUC Property; 4.3.3 An Efficient Pruning Algorithm; 4.3.4 Generalizing the UEUC Property; 4.4 A Framework for the Study of Measures; 4.4.1 Adapted Functions of Measure; 4.4.1.1 Association Rules; 4.4.1.2 Contingency Tables; 4.4.2 Expression of a Set of Measures of Ddconf; 4.5 Conditions for GUEUC; 4.5.1 A Sufficient Condition; 4.5.2 A Necessary Condition; 4.5.3 Classification of the Measures; 4.6 Conclusion; References
5 Classification Techniques and Error Control in Logic MiningGiovanni Felici, Bruno Simeone, and Vincenzo Spinelli; 5.1 Introduction; 5.2 Brief Introduction to Box Clustering; 5.3 BC-Based Classifier; 5.4 Best Choice of a Box System; 5.5 Bi-criterion Procedure for BC-Based Classifier; 5.6 Examples; 5.6.1 The Data Sets; 5.6.2 Experimental Results with BC; 5.6.3 Comparison with Decision Trees; 5.7 Conclusions; References; Part III Classification Analysis; 6 An Extended Study of the Discriminant Random Forest; Tracy D. Lemmond, Barry Y. Chen, Andrew O. Hatch,and William G. Hanley
6.1 Introduction
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