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Robert Stahlbock, Sven F. Crone and Stefan Lessmann
i-xiii
Front matter
19-49
Part 1 / Confirmatory data analysis
Response-Based Segmentation Using Finite Mixture Partial Least Squares Theoretical Foundations and an Application to American Customer Satisfaction Index Data
1-15
Data Mining and Information Systems: Quo Vadis?
53-119
Part 2 / Knowledge discovery from supervised learning
53-74
Building Acceptable Classification Models
75-98
Mining Interesting Rules Without Support Requirement: A General Universal Existential Upward Closure Property
99-119
Classification Techniques and Error Control in Logic Mining
123-226
Part 3 / Classification analysis
123-146
An Extended Study of the Discriminant Random Forest
147-158
Prediction with the SVM Using Test Point Margins
159-192
Effects of Oversampling Versus Cost-Sensitive Learning for Bayesian and SVM Classifiers
193-226
The Impact of Small Disjuncts on Classifier Learning
229-313
Part 4 / Hybrid data mining procedures
229-253
Predicting Customer Loyalty Labels in a Large Retail Database: A Case Study in Chile
255-276
PCA-based Time Series Similarity Search
277-297
Evolutionary Optimization of Least-Squares Support Vector Machines
299-313
Genetically Evolved kNN Ensembles
317-350
Part 5 / Web-mining
317-334
Behaviorally Founded Recommendation Algorithm for Browsing Assistance Systems
335-350
Using Web Text Mining to Predict Future Events: A Test of the Wisdom of Crowds Hypothesis
353-387
Part 6 / Privacy-preserving data mining
353-373
Avoiding Attribute Disclosure with the (Extended) p-Sensitive k-Anonymity Model
375-387
Privacy-Preserving Random Kernel Classification of Checkerboard Partitioned Data
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