Approximate Clustering on Data Streams Using Discrete Cosine Transform

Feng Yu, Damalie Oyana, Wen-Chi Hou and Michael Wainer
Volume: 6, No: 1, Page: 67 ~ 78, Year: 2010
10.3745/JIPS.2010.6.1.067
Keywords: Grid Density-Based Clustering, Approximate Cluster Analysis, Discrete Cosine Transform, Sampling, Data Reconstruction, Data Compression
Full Text:

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.

Article Statistics
Multiple requests among the same broswer session are counted as one view (or download).
If you mouse over a chart, a box will show the data point's value.


Cite this article
IEEE Style
Feng Yu, Damalie Oyana, Wen-Chi Hou and Michael 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, "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.