An Incremental Clustering Algorithm Based on Sample Selection

Internet users have to face to tremendous information from website. Clustering is a good solution to organize information. However, most clustering algorithms operate in the static situation. That means, it doesn't allow any incremental data. Certainly, this restrict is not fit to network environment, since data from internet is continuous increasing. Thus, an incremental clustering algorithm based on self-organizing-mapping is proposed in this paper. This algorithm has two innovations. (a) It integrates feature's ability in similarity measurement. (b) Based on the devised similarity measurement, this algorithm selects few samples from original texts to perform incremental clustering. Experiments demonstrate that, after integrating feature's ability in similarity measurement, our algorithm can obtain high clustering performance.