An Improved Method of Fuzzy Clustering Algorithm and Its Application in Text Clustering

摘要

An improved genetic clustering algorithm based on immune genetic algorithm with Fuzzy c-means algorithm is developed after analyzing the advantages and disadvantages of Fuzzy C-means algorithm and the genetic algorithm-based clustering algorithm. This algorithm uses the fuzzy clustering of Immune Partheno-Genetic to guide the number and the choice of the clustering centers. And then utilize FCM to make the clustering (IPGA-FCM). It uses standard genetic algorithm to overcomes the problem of local optimum and the contradiction of searing speed and clustering precision. Then it applies the novel clustering algorithm to Chinese document clustering. The clustering algorithm is superior to other ordinary clustering algorithm and the result can embody the wide diversity and large amount of Chinese document. Theoretical analysis and experiments show that this algorithm is more efficient.