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A Novel Method of Data Stream Clustering Based on Wavelet Timing Series Tree Synopsis |
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PP: 1077-1085 |
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Author(s) |
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Dongsheng Liu,
Chonghuan Xu,
Shujiang Fan,
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Abstract |
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| For the difficulty of obtaining cluster result fast and effectively under the limitations of bounded memory and time, this paper
proposes a novel data stream clustering method based on wavelet timing series tree synopsis to solve the problem. The proposed method
considers the attenuation characteristic of data stream, which combines the dynamic maintenance of wavelet coefficient and attenuation
feature of wavelet coefficients of data stream, and can achieve approximate representation of data stream fragment information and
dynamic maintenance of its synopsis structure. The proposed method employs wavelet timing series tree synopsis method to compress
data stream fragment, then adopts two-phase density clustering algorithm to cluster. Detailed experiments show that the proposed
method can get high compression quality, good space and time efficiency and good clustering results. |
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