Abstract:

Clustering analysis is an important tool of data mining. Enlightened by the collection behaviors of a flock of birds, a new data clustering algorithm  named Discriminating Dissonant Particle Swarm Clustering (DPSC) is presented. The DPSC algorithm changes data samples into a dynamical particle swarm, promotes the inhomogeneous particles to separate and the congeneric particles to collect. The structural features of the complex dataset will be emerged during the movement of particles, and the result of data object clustering is therefore achieved. Through experiments implemented on three standard datasets and six artificial complex datasets, the results show that the DPSC algorithm is more effective than the KMeans, PSO and PSO+KMeans algorithms.

Key words: data mining;clustering analysis;dissonant particle swarm algorithm