Improved particle swarm optimization and K-means clustering algorithm for news article

摘要

Fuzzy optimization based Data clustering is one of the important data mining tool, which is dynamic research of real world problems. K-Means algorithm is the most popular clustering method, because it is very easy to implement and fast working in the most of the situation. However this K-means algorithm is sensitive to initialization and easily trapped in local optima. Particle swarm optimization (PSO) is one of the global optimization techniques to solve most of the optimized problem. In this present trend, there has been an increasing interest in the application of the fuzzy model which gives the promising and efficient results if the data sets are too complex to analyze or available information is inexact or indecisive. This paper proposed an improved PSO algorithm with K-Means algorithm for NEWS articles clustering. So this algorithm can get advantage of both methods of PSO and K-Means. The experimental results shown the proposed method is efficient and provide best clustering results in few numbers of iterations. This algorithm is applied for three different types of data set.