Data Mining: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification

By Dawn E. Holmes

Book Overview

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.

• Format: Paperback
• Language: English
• ISBN: 3642430937
• ISBN13: 9783642430930
• Release Date: January 2014
• Publisher: Springer
• Length: 336 Pages
• Weight: 1.09 lbs.
• Dimensions: 0.7" x 6.1" x 9.2"