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Clustering by Integrating Multi-objective Optimization with Weighted K-Means and Validity Analysis.
- Source :
- Intelligent Data Engineering & Automated Learning - IDEAL 2006; 2006, p454-463, 10p
- Publication Year :
- 2006
-
Abstract
- This paper presents a clustering approach that integrates multi-objective optimization, weighted k-means and validity analysis in an iterative process to automatically estimate the number of clusters, and then partition the whole given data to produce the most natural clustering. The proposed approach has been tested on real-life dataset; results of both weighted and unweighed k-means are reported to demonstrate applicability and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540454854
- Database :
- Complementary Index
- Journal :
- Intelligent Data Engineering & Automated Learning - IDEAL 2006
- Publication Type :
- Book
- Accession number :
- 32914184
- Full Text :
- https://doi.org/10.1007/11875581_55