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 complaints of the seventh foreign convention on complicated information Mining and purposes, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised complete papers and 29 brief papers awarded including three keynote speeches have been rigorously reviewed and chosen from 191 submissions. The papers disguise quite a lot of issues offering unique examine findings in information mining, spanning functions, algorithms, software program and platforms, 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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Waiyamai Fig. 5. Purity and f-measure with progression of stream, horizon =2 Fig. 6. Purity and f-measure with progression of stream, horizon =500 Fig. 7. Purity and f-measure with increase in uncertain level, horizon =2 Evolution-Based Clustering Technique for Heterogeneous Data Streams 39 Fig. 8. Sensitivity with number of cluster, horizon =2 Fig. 9. Efficiency of stream clustering, horizon =2 linear runtime in number of data points. However, efficiency of UMicro is higher than that of HUE-Stream with a constant.

Tang et al. ): ADMA 2011, Part II, LNAI 7121, pp. 27–40, 2011. © Springer-Verlag Berlin Heidelberg 2011 28 W. Meesuksabai, T. Kangkachit, and K. Waiyamai To support uncertainty in data streams, Aggarwal et al. [4] introduced the uncertain clustering feature for cluster representation and proposed a technique named UMicro. Later, they continued to study the problem of high dimensional projected clustering of uncertain data streams [1]. LuMicro [5] technique has been proposed to improve clustering quality, with the support of uncertainty in numerical attributes and not in categorical attributes.

Since CDE- EM- BM often chooses inappropriate model built from class distribution different from test data than desired. CDE-EM-AVGBM method while not perform badly like CDE- EM- BM but still not show any sign of improvement neither. We presume this happen because the error in model prediction that mention earlier have negative impact on this method even if using average value improve some accuracy of it. We found CDE-EM-AVG performance is better than above methods. This means using average value can improve overall accuracy.

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