Improved PSO-based fast clustering algorithm
Donghui Chen · Journal of Xidian University · 2012
This paper presents an improved particle swarm optimization based fast K-means algorithm which effectively overcomes the shortcomings of the K-means algorithm such as sensitive to initial cluster centroid and easiness to fall into local optimum so as to affect the clustering results.Compared with the existing particle clustering algorithm,is algorithm first normalizes the attributes of all the samples,and then computes the dissimilarity matrix.We propose simplified particle encoding rules and use PSO-based K-means clustering based on the dissimilarity matrix to ensure the basis for the clustering effect and reduce computational complexity.Experimental results on several UCI data sets validate the advantages of the proposed algorithm.