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 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