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 lawsuits of the seventh foreign 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 provided including three keynote speeches have been conscientiously reviewed and chosen from 191 submissions. The papers disguise quite a lot of themes featuring unique study findings in info mining, spanning purposes, algorithms, software program and structures, and utilized disciplines.
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Extra info for Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part II
The outer AIA is used to find the optimal parameter settings (such as Qi , g and β ) of the inner CMAC NN classifier, and the inner CMAC NN classifier is applied to solve benchmark classification problems. The performance of the AIAMIMO CMAC NN classifier is compared with that of published classifiers from the literature [8, 12, 14, 15, 18-21]. 1 MIMO CMAC NN Classifier Figure 1 shows the network topology of the MIMO CMAC NN classifier for solving classification problems. 5 Yd ,2 = [ yd ,21 , yd ,22 ,… , yd ,2 ntrain ]T j = 1, 2,… , ntrain yo, pj = actual output from output neuron p of input pattern j p = 1, 2,… , pmax yd , pj = desired output of from desired vector p of input pattern j g = generalization size ntrain = number of input patterns for training Fig.
3. U 4 if dist[i] < radius_factor/4 4 return i 5 set_candidate ← i 6 return set_candidate Fig. 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.
Keywords: Uncertain data streams, Heterogeneous data, Clustering, Evolutionbased clustering. 1 Introduction Recently, clustering data streams has become a research topic of growing interest. One main characteristic of data streams is to have the infinite evolving structure and to be generated at rapid rate. 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.