Approximate Clustering on Data Streams Using Discrete Cosine Transform


Feng Yu, Damalie Oyana, Wen-Chi Hou, Michael Wainer, Journal of Information Processing Systems
Vol. 6, No. 1, pp. 67-78, Feb. 2010
10.3745/JIPS.2010.6.1.067
Keywords: Grid Density-Based Clustering, Approximate Cluster Analysis, Discrete Cosine Transform, Sampling, Data Reconstruction, Data Compression
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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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Cite this article
[APA Style]
Feng Yu, Damalie Oyana, Wen-Chi Hou, & Michael Wainer (2010). Approximate Clustering on Data Streams Using Discrete Cosine Transform. Journal of Information Processing Systems, 6(1), 67-78. DOI: 10.3745/JIPS.2010.6.1.067.

[IEEE Style]
F. Yu, D. Oyana, W. Hou and M. Wainer, "Approximate Clustering on Data Streams Using Discrete Cosine Transform," Journal of Information Processing Systems, vol. 6, no. 1, pp. 67-78, 2010. DOI: 10.3745/JIPS.2010.6.1.067.

[ACM Style]
Feng Yu, Damalie Oyana, Wen-Chi Hou, and Michael Wainer. 2010. Approximate Clustering on Data Streams Using Discrete Cosine Transform. Journal of Information Processing Systems, 6, 1, (2010), 67-78. DOI: 10.3745/JIPS.2010.6.1.067.