MOEA for clustering: comparison of mutation operators
Pages 91 - 92
Abstract
Clustering is an important task in data mining. However, there are numerous conflicting measurements of what a good clustering solution is. Therefore, clustering is a task that is suitable for a Multi-Objective Evolutionary Algorithm. Mutation operators for these algorithms can be designed to explore a diverse range of solutions or focus upon individual solution quality. We propose using a hybrid technique that generates a wide range of solutions and then improves them with respect to the data. We create an experimental set-up to assess mutation operators with respect to Pareto front quality. Using this set-up we find that mutation operators that mutate solutions with respect to the data perform better but hybrid mutation techniques show promise.
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Index Terms
- MOEA for clustering: comparison of mutation operators
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Published: 06 July 2013
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July 6 - 10, 2013
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