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Fuzzy Clustering for Missing Data Based on Particle Swarm Optimization and Artificial Bee Colony Algorithm

C. Y. Liu


The existing fuzzy clustering don’t effectively dispose the missing data, and proposes a fuzzy clustering approach for missing data based on Particle Swarm Optimization and Artificial Bee Colony Algorithm (PSOABC-FC). Use the interval to process missing data by searching the nearest samples, so the traditional fuzzy clustering method can cope with missing data. Divide the population into two subgroup. Subgroup one use the particle swarm optimization method to search, and Subgroup two use the artificial bee colony. In order to avoid falling into the local optimum, two subgroups communicate information in the evolutionary process. The experimental results show, compared to the other clustering methods for missing data, PSOABC-FC has the higher accuracy of classification, at the same times, it has more average accuracy rate for clustering and the less average intra-class distance.


Fuzzy clustering, particle swarm optimization, artificial bee colony, missing Data.

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