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 purposes, 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 a variety of issues proposing unique learn findings in info mining, spanning purposes, algorithms, software program and structures, and utilized disciplines.

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Additional info 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 propose four methods which are CDE-EMAVG-N, CDE-EM-BM-N, CDE-EM-AVGBM-M-N and CDE-EM-EX-N. CDE-EM-AVG-N builds multiple models with different class distribution then uses average value obtained from these models to estimate class distribution of test data. Instead of using average value from many models, CDE-EM-BM-N selects the best model which has been built from the most similar class distribution to test data. While this method may sound promising, it has problem about the correct model selection having class distribution similar to test data, thus leads to lower accuracy compared to other methods.

Section 2 introduces basic concepts and definitions. Section 3 presents our stream clustering algorithm called HUE-Stream. Section 4 compares the performance of HUE-Stream and UMicro with respect to the real-world dataset. Conclusions are discussed in Section 5. 2 Basic Concepts of Evolution-Based Stream Clustering with Uncertainty In the following, some notations and definitions of evolution-based stream clustering with uncertainty are defined. First, we assume that we have a data stream consists in a set of dimensional tuples … … arriving at time stamps … ….

897–906. ACM, Paris (2009) 20. : Learning and making decisions when costs and probabilities are both unknown. In: The Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining, pp. th Abstract. Evolution-based stream clustering method supports the monitoring and the change detection of clustering structures. E-Stream is an evolutionbased stream clustering method that supports different types of clustering structure evolution which are appearance, disappearance, self-evolution, merge and split.

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