Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Facing classification problems with Particle Swarm Optimization
Applied Soft Computing
A review on particle swarm optimization algorithms and their applications to data clustering
Artificial Intelligence Review
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As DNA chips are becoming increasingly popular as a convenient way to perform vast amounts of experiments related to genes on a single chip, the importance of analyzing the data that is provided by such DNA chips is becoming significant. A very important analysis on DNA chip data would be clustering genes to identify gene groups which have similar properties. Clustering data for DNA chips usually deal with a large search space and has a very fuzzy characteristic. This task can be achieved by applying a clustering technique that mimics the biological world. One such algorithm is the Particle Swarm Optimization algorithm which was recently proposed as a very good candidate to solve such problems. In this paper, we propose a clustering mechanism that is based on the Particle Swarm Optimization algorithm. Our experiments show that the PSO-based clustering algorithm developed can efficiently cluster DNA chip data, and thus be used to extract valuable information from DNA chip data in an accurate yet timely manner.