Data Mining: Foundations and Intelligent Paradigms [electronic resource] : Volume 1: Clustering, Association and Classification / edited by Dawn E. Holmes, Lakhmi C. Jain.

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.

Data mining is one of the most rapidly growing research areas in computer science and statistics. In Volume 1of this three volume series, we have brought together contributions from some of the most prestigious researchers in the fundamental data mining tasks of clustering, association and classification. Each of the chapters is self contained. Theoreticians and applied scientists/ engineers will find this volume valuable. Additionally, it provides a sourcebook for graduate students interested in the current direction of research in these aspects of data mining.

ZDB-2-ENG