Particle swarm optimization based fuzzy frequent pattern mining from gene expression data

The FP-growth algorithm is currently one of the fastest approaches to frequent item set mining. Fuzzy logic provides a mathematical framework where the entire range of the data lies in between 0 and 1. The PSO algorithm was developed from observations of the social behavior of animals, including bird flocking and fish schooling. It is easier to implement than evolutionary algorithms because it only involves a single operator for updating solutions. In contrast, evolutionary algorithms require a particular representation and specific methods for cross-over, mutation, and selection. Furthermore, PSO has been found to be very effective in a wide variety of applications, being able to produce good solutions at a very low computational cost. In this paper, we have considered the fuzzified dataset and have implemented various frequent pattern mining techniques. Out of the various frequent pattern mining techniques it was found that Frequent Pattern (FP) growth method yields us better results on a fuzzy dataset. Here, the frequent patterns obtained are considered as the set of initial population. For the selection criteria, we had considered the mean squared residue score rather using the threshold value. It was observed that out of the four fuzzy based frequent mining techniques, the PSO based fuzzy FP growth technique finds the best individual frequent patterns. Also, the run time of the algorithm and the number of frequent patterns generated is far better than the rest of the techniques used.