College of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, China; Center for Studies of Modern Business, Zhejiang Gongshang University, Hangzhou 310018, China

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Abstract—With the limited memory and time, a fast and effective clustering can’t be achieved for massive, highspeed data stream, so this paper mainly studies the key method of data stream clustering under the restriction of resource, and then proposes a dynamic data stream clustering algorithm (D-DStream) based on wavelet network and density, which uses sliding window to process data stream. Firstly, apply wavelet network to compress data stream and build a much smaller synopsis data structure to save major characteristics of data stream, then cluster with two-phase density clustering algorithm. The results of experiment show that the D-DStream algorithm can successfully solve clustering problems caused by STREAM or others, also has high time efficiency and high clustering quality.

Index Terms—Data Stream Clustering, Wavelet Network, Two-phase Density Clustering, Sliding Window

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Cite: Chonghuan Xu, " A Novel Spatial Clustering Method based on Wavelet Network and Density Analysis for Data Stream," Journal of Computers vol. 8, no. 8, pp. 2139-2143, 2013.