Grid-based clustering over an evolving data stream Online publication date: Thu, 29-Oct-2009
by Renxia Wan, Jingchao Chen, Lixin Wang, Xiaoke Su
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 1, No. 4, 2009
Abstract: Clustering on data stream has a great challenge because it has to be implemented within a limited space and a strict time constraint and the data stream may be potentially infinite. Fortunately, many clustering algorithms for data stream have been proposed, these algorithms have greatly promoted the clustering level of data stream, but most of the algorithms are designed for convex clusters. In this paper, a grid-based clustering algorithm is presented, it maps every data into the corresponding grid firstly and then iteratively merges these grids into clusters via merging steps, only boundary grids are considered during the merging stage. The algorithm also can group the evolving data stream into arbitrary shaped clusters. Compared with the same categorical algorithms, it has a less parameters input. In terms of effectivity and efficiency, the proposed algorithm outperforms the same categorical ones from theoretical and experimental analysis.
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