K-medoids Text Clustering Algorithm Based On BFO

Text clustering is an effective text management method. It has become an effectiveorganization, integration, summary and important method of text retrieval. k-medoidsclustering algorithm is a very important division of the text-based clustering algorithm,applied in many ways. But the algorithm has some shortcomings, the choice of initial clustercenters is random, and the algorithm is easy to terminate in a local optimum. So this willaffect the algorithm to generate the clustering effect.Bacterial foraging optimization algorithm which is an optimization algorithm and abionic optimization algorithm, simulated foraging behavior of colon bacillus, includingchemotaxis, reproduction and migration operations. This article is combining the bacteriaforaging with k-medoids algorithm used in text clustering algorithm, in order to improve thek-medoids method, bacterial foraging optimization algorithm optimization function to find thebest initial cluster centers, thereby overcoming randomly chosen initial cluster centersshortcomings, and can jump out of the local optimum. Experimental results show thefeasibility of the proposed method .