Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Approximate Clustering on Data Streams Using Discrete Cosine Transform

  • Yu, Feng (Department of Computer Science, Southern Illinois University) ;
  • Oyana, Damalie (Department of Computer Science, Southern Illinois University) ;
  • Hou, Wen-Chi (Department of Computer Science, Southern Illinois University) ;
  • Wainer, Michael (Department of Computer Science, Southern Illinois University)
  • Published : 2010.03.31
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Abstract

In this study, a clustering algorithm that uses DCT transformed data is presented. The algorithm is a grid density-based clustering algorithm that can identify clusters of arbitrary shape. Streaming data are transformed and reconstructed as needed for clustering. Experimental results show that DCT is able to approximate a data distribution efficiently using only a small number of coefficients and preserve the clusters well. The grid based clustering algorithm works well with DCT transformed data, demonstrating the viability of DCT for data stream clustering applications.

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References

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