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, Mar. 2010  

10.3745/JIPS.2010.6.1.067
Keywords: Grid Density-Based Clustering, Approximate Cluster Analysis, Discrete Cosine Transform, Sampling, Data Reconstruction, Data Compression
Fulltext:

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.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Yu, F., Oyana, D., Hou, W., & Wainer, M. (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, 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.