Advanced Data Mining and Applications: 7th International by Liang Wu, Yuanchun Zhou, Fei Tan, Fenglei Yang (auth.), Jie

By Liang Wu, Yuanchun Zhou, Fei Tan, Fenglei Yang (auth.), Jie Tang, Irwin King, Ling Chen, Jianyong Wang (eds.)

The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed court cases of the seventh overseas convention on complicated info Mining and functions, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised complete papers and 29 brief papers offered including three keynote speeches have been conscientiously reviewed and chosen from 191 submissions. The papers conceal quite a lot of subject matters offering unique learn findings in facts mining, spanning purposes, algorithms, software program and structures, and utilized disciplines.

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Extra resources for Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part II

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We call a stream clustering method that supports the monitoring and the change detection of clustering structures evolution-based stream clustering method. Apart from its infinite data volume, data streams also contain error or only partially complete information, called data uncertainty. In this paper, we focus on developing an evolution-based stream clustering method that supports uncertainty in data. Many techniques have been proposed for clustering data streams. Most research has focused on clustering techniques for numerical data [1, 2, 3, 7].

4. MergeOverlapCluster, CheckSplit, FindCandidateClosestCluster and FindClosestCluster In the following, details of each step are described. FadingAll performs fading of all the existing clusters in the system. New data points are preferred to old data points. W < fade_threshild), it will be deleted from the system. 36 W. Meesuksabai, T. Kangkachit, and K. Waiyamai LimitMaximumCluster: This procedure is used to limit the number of cluster. It checks whether the number of clusters is not greater than its “maximum_cluster”.

Paper presented at the Proceedings of the 29th International Conference on Very Large Data Bases, Berlin, Germany, vol. 29 (2003) 3. : A framework for projected clustering of high dimensional data streams. Paper presented at the Proceedings of the Thirtieth International Conference on Very Large Data Bases, Toronto, Canada, vol. 30 (2004) 4. : A Framework for Clustering Uncertain Data Streams. In: IEEE 24th International Conference on Data Engineering, ICDE 2008, April 7-12, pp. 150–159 (2008) 5.

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