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.