关键词: 不确定数据流, 粗糙模糊集, 聚类, 隶属度

Abstract: To solve data streams clustering problems of high dimensionality and high uncertainty level, this paper proposes an algorithm named HFMicro. The rough fuzzy set theory is introduced to define a new uncertain model of data streams, and the upper and lower approximations of the membership degree are used to describe micro-clusters. The most suitable micro-clusters are selected according to the similarity degree between rough fuzzy sets. Dynamic window of decay model is applied to achieve good algorithmic efficiency and clustering performance. Offline clustering model makes the algorithm have good real-time performance. The experimental results show that the algorithm can handle the data streams with high dimensionality and uncertainty level, and can process the data streams having existent uncertainty and property uncertainty at the same time. In comparison with the existing algorithms, HFMicro has better performance.

Key words: uncertain data streams, rough fuzzy set, clustering, membership degree