Abstract
Data clustering is a good benchmark problem for testing the performance of many combinatory optimization methods. However, very few works have been done on using the estimation of distribution algorithms for solving the problem of data clustering. The purpose of this paper is to demonstrate the effectiveness of the estimation of distribution algorithms for solving the problem of data clustering. In particular, a novel encoding strategy termed as the Similarity Matrix Encoding strategy (SME) and a Virtual Population Based Incremental Learning algorithm using SME encoding strategy (VPBIL-SME) are proposed for clustering a set of unlabeled instances into groups. Effectiveness of VPBIL-SME is confirmed by experimental results on several real data sets.
Original language | English |
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Pages | 471-472 |
Number of pages | 2 |
DOIs | |
Publication status | Published - Jul 2008 |
Externally published | Yes |
Event | 10th Annual Genetic and Evolutionary Computation Conference - Atlanta, United States Duration: 12 Jul 2008 → 16 Jul 2008 |
Conference
Conference | 10th Annual Genetic and Evolutionary Computation Conference |
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Abbreviated title | GECCO 2008 |
Country/Territory | United States |
City | Atlanta |
Period | 12/07/08 → 16/07/08 |
Keywords
- Data clustering
- Similarity matrix encoding strategy
- Virtual population based incremental learning algorithm