Foundations of Data Mining and Knowledge Discovery (Studies in Computational Intelligence) (Studies in Computational Intelligence)

foundations Of Data Mining And Knowledge Discovery Contains The Latest Results And New Directions In Data Mining Research. Data Mining, Which Integrates Various Technologies, Including Computational Intelligence, Database And Knowledge Management, Machine Learning, Soft Computing, And Statistics, Is One Of The Fastest Growing Fields In Computer Science. Although Many Data Mining Techniques Have Been Developed, Further Development Of The Field Requires A Close Examination Of Its Foundations. This Volume Presents The Results Of Investigations Into The Foundations Of The Discipline, And Represents The State Of The Art For Much Of The Current Research. This Book Will Prove Extremely Valuable And Fruitful For Data Mining Researchers, No Matter Whether They Would Like To Uncover The Fundamental Principles Behind Data Mining, Or Apply The Theories To Practical Applications. Foundations of Data Mining and Knowledge Discovery......Page 1 Preface......Page 6 Contents......Page 12 Part I Foundations of Data Mining......Page 15 Knowledge Discovery as Translation......Page 17 Mathematical Foundation of Association Rules – Mining Associations by Solving Integral Linear Inequalities......Page 34 Comparative Study of Sequential Pattern Mining Models......Page 56 Designing Robust Regression Models......Page 84 A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases......Page 100 A Careful Look at the Use of Statistical Methodology in Data Mining......Page 114 Justification and Hypothesis Selection in Data Mining......Page 131 On Statistical Independence in a Contingency Table......Page 143 Part II Methods of Data Mining......Page 154 A Comparative Investigation on Model Selection in Binary Factor Analysis......Page 156 Extraction of Generalized Rules with Automated Attribute Abstraction......Page 172 Decision Making Based on Hybrid of Multi-Knowledge and Naive Bayes Classifier......Page 182 First-Order Logic Based Formalism for Temporal Data Mining......Page 196 An Alternative Approach to Mining Association Rules......Page 222 Direct Mining of Rules from Data with Missing Values......Page 243 Cluster Identification Using Maximum Configuration Entropy......Page 275 Mining Small Objects in Large Images Using Neural Networks......Page 287 Improved Knowledge Mining with the Multimethod Approach......Page 314 Part III General Knowledge Discovery......Page 328 Posting Act Tagging Using Transformation-Based Learning......Page 330 Identification of Critical Values in Latent Semantic Indexing......Page 341 Reporting Data Mining Results in a Natural Language......Page 355 An Algorithm to Calculate the Expected Value of an Ongoing User Session......Page 370