Data Mining: Foundations and Intelligent Paradigms

Introductory Chapter. - Clustering Analysis in Large Graphs with Rich Attributes. - Temporal Data Mining: Similarity-Profiled Association Pattern. - Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification. - Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets. - Randomized Algorithm of Finding the True Number of Clusters Based on Chebychev Polynomial Approximation. - Bregman Bubble Clustering: A Robust Framework for Mining Dense Clusters. - DepMiner: A method and a system for the extraction of significant dependencies. - Integration of Dataset Scans in Processing Sets of Frequent Itemset Queries. - Text Clustering with Named Entities: A Model, Experimentation and Realization. - Regional Association Rule Mining and Scoping from Spatial Data. - Learning from Imbalanced Data: Evaluation Matters.