Particle swarm optimization-based clustering and classification algorithms for analyzing DNA chip data

DNA chips are used for experiments on genes and provide useful information that could be further analyzed. Instead of experimenting with real genes which are expensive, DNA chips are increasingly being used for biological experiments. The data provided by the DNA chips could be represented as a two dimensional matrix, in which one axis represent genes and the other represent samples. Extracting data from the DNA chips with high accuracy and finding out the patterns or useful information from such data has become a very important issue. Some commonly used methods to find meaningful information from the data are clustering and classification. In this paper, we propose clustering and classification mechanisms that are based on the Particle Swarm Optimization algorithm. Considering that DNA chip data is extremely large and has a fuzzy characteristic, an algorithm that imitates the ecosystem such as the Particle Swarm Optimization algorithm is suitable to be used for analyzing such data. Experiments show that the algorithms can efficiently cluster and classify DNA chip data.