Using quantum particle swarm optimization to enhance k-means clustering

Abstract EN

Clustering isan unsupervised data mining technique used to extract a new knowledge.

It clusters a group of objects into clusters where objects in one cluster have similar fea- tures to each other and have different features from objects in other clusters.

K-means algorithm creates clusters by divides the data points into clusters according to similarity criterion.

The K-means algorithm select initial centroids randomly then slow convergence points to centroids.

This paper suggests a method for computing the initial centroids and fast convergence by using Quantum Particle Swarm Optimization with the global searching optimization which will give algorithm more efficient, so as to get quality clustering with reducedcomplexity.