Practical applications of data mining

Foreword / Murat M. Tanik -- Foreword / John Kocur -- Introduction to data mining. -- Traditional database management systems -- Knowledge discovery in databases -- Data-mining methods -- Integrated framework for intelligent databases -- Practical applications of data mining -- Association rules. -- Introduction -- Mining of association rules in market basket data -- Attribute-oriented rule generalization -- Association rules in hypertext databases -- Quantitative associaton rules -- Mining of compact rules -- Mining of time-constrained association rules -- Classification learning. -- Introduction -- Knowledge representation -- Separate-and-conquer approach -- Divide-and-conquer approach -- Partial decision tree -- Statistics for data mining. -- Introduction -- House sales data -- Conditional probability -- Equality tests -- Correlation coefficient -- Contingency table and the x² test -- Linear regression -- House sales database revisited -- Rough sets and Bayes' Theories. -- Introduction -- Bayes' Theorem -- Rough sets -- Applications based on Bayes' and rough sets -- Neural networks. -- Introduction -- Neural computing and databases -- Network classification -- Parameters of the learning process -- Network structures -- Knowledge discovery in databases -- Backpropagation neural network (BPNN) model -- Bidirectional associative memory (BAM) model -- Learning vector quantization (LVQ) model -- Probabilistic neural network (PNN) model -- Clustering. -- Introduction -- Definition of clusters and clustering -- Clustering procedures -- Clustering concepts -- Clustering algorithms -- Fuzzy informational retrieval. -- Introduction -- Fuzzy set basics -- Fuzzy set applications -- Linguistic variables -- Fuzzy querry processing -- Fuzzy querry processing using fuzzy tables -- Role of relational division for information retrieval -- Alpha-cut thresholds -- Appendix

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