Clustering by a genetic algorithm with biased mutation operator
2010 (English)In: 2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE , 2010, 1-8 p.Conference paper (Refereed)
In this paper we propose a genetic al- gorithm that partitions data into a given number of clusters. The algorithm can use any cluster validity function as fitness function. Cluster validity is used as a criterion for cross-over operations. The cluster assignment for each point is accompanied by a tem- perature and points with low confidence are pref- erentially mutated. We present results applying this genetic algorithm to several UCI machine learning data sets and using several objective cluster validity functions for optimization. It is shown that given an appropriate criterion function, the algorithm is able to converge on good cluster partitions within few generations. Our main contributions are: 1. to present a genetic algorithm that is fast and able to converge on meaningful clusters for real-world data sets, 2. to define and compare several cluster validity criteria.
Place, publisher, year, edition, pages
IEEE , 2010. 1-8 p.
learning (artificial intelligence), pattern clustering, UCI machine learning, cluster validity function, criterion function, crossover operation, fitness function, genetic algorithm, mutation operator, optimization
Research subject SRA - ICT
IdentifiersURN: urn:nbn:se:kth:diva-48065DOI: 10.1109/CEC.2010.5586090ISI: 000287375801062OAI: oai:DiVA.org:kth-48065DiVA: diva2:456742
2010 IEEE World Congress on Computational Intelligence. Barcelona, SPAIN. JUL 18-23, 2010
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