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Approximate Clustering on Data Streams Using Discrete Cosine Transform
Yu, Feng,Oyana, Damalie,Hou, Wen-Chi,Wainer, Michael Korea Information Processing Society 2010 Journal of information processing systems Vol.6 No.1
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.