Feature Weighting for Clustering by Particle Swarm Optimization

Clustering has been the most popular method for data exploration. Clustering is partitioning the data set into sub-partitions based on some measures say the distance measure, each partition has its own significant information. There are a number of algorithms explored for this purpose, one such algorithm is the Particle Swarm Optimization(PSO) which is a population based heuristic search technique derived from swarm intelligence. in this paper we present an improved version of the Particle Swarm Optimization where, each feature of the data set is given significance accordingly by adding some random weights, which also minimizes the distortions in the dataset if any. the performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. the experimental results shows that our proposed methodology performs significantly better than the previously performed experiments.