关键词: 数据流, 聚类, 高维, 子空间, 数据挖掘

Abstract: A new method for estimating probability density of data distribution on data streams which is a more reasonable strategy for fading the old data is proposed. Based on Parzen method, the information entropy of the subspace of the data set can be calculated. Based on the close relationship between entropy and distribution, an effective algorithm based on entropy for clustering high dimensional data streams called PStream is also developed. The theoretical and simulation results show that compared with the previous results, PStream algorithm scans over the data stream in only a single pass and has a high clustering precision although it is not much more efficient than the previous method such as HPStream. 

Key words: data streams, clustering, high dimension, subspace, data mining