Feature weighting in κ-means clustering

Details Feature weighting in κ-means clustering Feature weighting in κ-means clustering

2005-10-04

2005-10-04

Le, Uyen (Department: 2163)

Data processing: database and file management or data structures

Database design

Data structure types

C707S793000

Reexamination Certificate

active

06952700

ABSTRACT:
A method and system is provided for integrating multiple feature spaces in a k-means clustering algorithm when analyzing data records having multiple, heterogeneous feature spaces. The method assigns different relative weights to these various features spaces. Optimal feature weights are also determined that lead to a clustering that simultaneously minimizes the average intra-cluster dispersion and maximizes the average inter-cluster dispersion along all the feature spaces. Examples are provided that empirically demonstrate the effectiveness of feature weighting in clustering using two different feature domains.

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Ben-Tal et al “Rate distortion theory with generalized information measures via convex programming duality”, IEEE pp. 630-641.

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