Holmes Dawn E., Jain Lakhmi C. (eds.) Data Mining: Foundations and Intelligent Paradigms. Volume 1. Clustering, Association and Classification

Springer, 2012. — 336 p. — (Intelligent Systems Reference Library Series, vol. 23). — ISBN: 978-3-642-23166-7; ISBN: 978-3-642-23165-0; ISBN: 978-3-642-43093-0.There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.Data Mining Techniques in Clustering, Association and Classification
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