Data Mining: Foundations and Intelligent Paradigms

1. Data Mining Techniques in Clustering, Association and Classification
Dawn E. Holmes, Jeffrey Tweedale, Lakhmi C. Jain

2. Clustering Analysis in Large Graphs with Rich Attributes
Yang Zhou, Ling Liu

3. Temporal Data Mining: Similarity-Profiled Association Pattern
Jin Soung Yoo

4. Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification
G. Corani, A. Antonucci, M. Zaffalon

5. Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets
Fionn Murtagh, Pedro Contreras

6. Randomized Algorithm of Finding the True Number of Clusters Based on Chebychev Polynomial Approximation
R. Avros, O. Granichin, D. Shalymov, Z. Volkovich, G. -W. Weber

7. Bregman Bubble Clustering: A Robust Framework for Mining Dense Clusters
Joydeep Ghosh, Gunjan Gupta

8. DepMiner: A Method and a System for the Extraction of Significant Dependencies
Rosa Meo, Leonardo D’Ambrosi

9. Integration of Dataset Scans in Processing Sets of Frequent Itemset Queries
Marek Wojciechowski, Maciej Zakrzewicz, Pawel Boinski

10. Text Clustering with Named Entities: A Model, Experimentation and Realization
Tru H. Cao, Thao M. Tang, Cuong K. Chau

11. Regional Association Rule Mining and Scoping from Spatial Data
Wei Ding, Christoph F. Eick

12. Learning from Imbalanced Data: Evaluation Matters
Troy Raeder, George Forman, Nitesh V. Chawla

Keywords: Engineering, Computational Intelligence, Artificial Intelligence (incl. Robotics)