基于相对密度的数据流模糊聚类算法

Data Stream Fuzzy Clustering Algorithm Based on Relative Density

提出的基于相对密度的数据流模糊聚类算法结合了相对密度聚类和模糊聚类的优点,能形成任意形状、多密度分辨率的层次聚类结果。同时,利用微簇空间位置重叠关系,定义了微簇集合间的差运算,从而有效地支持了用户指定时间窗口内的数据流聚类要求。通过与CluStream算法在聚类质量和处理时间两个方面的比较分析,发现基于相对密度的数据流模糊聚类算法具有明显的优势。

This paper provided a relative density based data stream fuzzy clustering algorithm which inherits the advantages of relative density based clustering and fuzzy clustering, so it can discover arbitrary-shape and multi-resolution clusters. With the subtraction operator on the set of micro-clusters which is defined according to the spatial overlapping relations among micro-clusters, this algorithm can do clustering on any user-specified data stream window. Compared with CluStream algorithm on the two areas of clustering quality and processing time, this algorithm demonstrates a clear advantage.