Hybrid clustering for large sequential data
Yang, Jianhua, and Lee, Ickjai (2007) Hybrid clustering for large sequential data. In: Proceedings of the International Conference on Artificial Intelligence and Pattern Recognition 2007, pp. 76-81. From: AIPR-07 - International Conference on Artificial Intelligence and Pattern Recognition, 9-12 July 2007, Orlando, Florida, USA.
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Many scientific and commercial domains have witnessed enormous data explosion that has inherent sequential nature. While clustering sequential data is useful for various purposes, there has been less success due to the discrete nature of sequential data. We combine techniques from data mining and computational geometry to efficiently and effectively segment sequential web usage data in data-rich environments. We provide an hybrid O(n n) clustering algorithm that combines medoid-based partitioning and agglomerative hierarchial clustering. This hybridization is inspired by geometrical and topological aspects of the Voronoi diagram. Experimental results demonstrate the superiority of our hybridization over traditional approaches.
|Item Type:||Conference Item (Refereed Research Paper - E1)|
|Keywords:||clustering; sequential clustering|
|Date Deposited:||22 Oct 2009 04:58|
|FoR Codes:||08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified @ 100%|
|SEO Codes:||89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890202 Application Tools and System Utilities @ 50%
89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890205 Information Processing Services (incl. Data Entry and Capture) @ 40%
89 INFORMATION AND COMMUNICATION SERVICES > 8999 Other Information and Communication Services > 899999 Information and Communication Services not elsewhere classified @ 10%