Grouping Multidimensional Data Recent Advances in Clustering /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Kogan, Jacob (Editor), Nicholas, Charles (Editor), Teboulle, Marc (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.
• Subjects: Computer science.; Data structures (Computer science).; Computer science > Mathematics.; Information storage and retrieval.; Pattern recognition.; Statistics.; Computer Science.; Data Structures, Cryptology and Information Theory.; Information Storage and Retrieval.; Statistical Theory and Methods.; Math Applications in Computer Science.; Statistics and Computing/Statistics Programs.; Pattern Recognition.

Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anom...

Table of Contents:

• The Star Clustering Algorithm for Information Organization
• A Survey of Clustering Data Mining Techniques
• Similarity-Based Text Clustering: A Comparative Study
• Clustering Very Large Data Sets with Principal Direction Divisive Partitioning
• Clustering with Entropy-Like k-Means Algorithms
• Sampling Methods for Building Initial Partitions
• TMG: A MATLAB Toolbox for Generating Term-Document Matrices from Text Collections
• Criterion Functions for Clustering on High-Dimensional Data.