Approximate Clustering on Data Streams Using Discrete Cosine Transform
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초록
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.
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.
참고문헌 (15)
- C. C. Aggarwal, "A Frame-work for Projected Clustering of High Dimension Data Streams", VLDB Conference (2004)
- C. C. Aggarwal, "A Frame-work for Clustering Evolving Data Streams", VLDB Conference (2003)
- M. Ester, "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise", Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD'96), pp.226~231 (1996)
- D.Fisher, "Iterative Optimization and Simplification of Hierarchical Clusterings", Journal of AI Research, 4, pp.147~180 (1996)
- Z. Fu, "Mixture Clustering Using Multidimensional Histograms for Skin Detection", ICPR, 4, pp.549~552 (2004)
- Gaber, M. M, "Mining data streams: a review", SIGMOD Rec, 34(2), pp.18~26 (2005)
- S. Guha, "Clustering Data Streams", IEEE FOCS Conference (2000)
- S. Guha, "CURE: An Efficient Clustering Algorithm for Large Databases", ACM SIGMOD Conference (1998)
- Y. Lu, "Mining Data Streams Using Clustering", Proc. 4th Int. Conf. on Machine Learning and Cybernetics, pp.18~21 (2005)
- J. Lee, "-Dimensional Selectivity Estimation Using Compressed Histo-gram Information", Proc. ACM SIGMOD Conf, pp.205~214 (1999)
- G. Medhat, M, "Towards an Adaptive Approach for Mining Data Streams in Resource Constrained Environments", Proc. of 6th Int. Conf. on Data Warehousing and Knowledge Discovery – Industry Track (DaWak 2004) (2004), Springer Verlag
- L. O'Callaghan, "Streaming-Data Algorithms for High-Quality Clustering", ICDE Conference (2002)
- N. Park, "Statistical Grid-Based Clustering over Data Streams", ACM SIGMOD Record, 33(1), pp.32~37
- V. Poosala, "Improved Histograms for Selectivity Estimation of Range Predicates", ACM SIGMOD, ~ (1996)
- G.Strang, "The Discrete Cosine Transform", SLAM Review, 41(1), pp.135~147 (1999)