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 foreign convention on complex 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 hide a variety of subject matters providing unique study findings in facts mining, spanning functions, 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
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. 40 5 W. Meesuksabai, T. Kangkachit, and K. Waiyamai Conclusions The uncertainty in the data stream significantly affects the clustering structure.
3 Distance Functions A distance function plays important role in data clustering tasks. To deal with uncertainty in both categorical and numerical data, we propose new distance functions that take into account the uncertainty as further described as follows. 4 Evolution-Based Stream Clustering Evolution-based stream clustering method supports the monitoring and the change detection of clustering structures. A cluster is a collection of data that have been memorized for processing in the system.
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.