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 complex facts Mining and functions, 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 a variety of themes featuring unique examine findings in information mining, spanning purposes, algorithms, software program and structures, and utilized disciplines.
Read or Download Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part II PDF
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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
Moreover, finding the parameter settings of the MIMO CMAC NN classifier can be considered to be equivalent to solving an unconstrained global optimization (UGO) problem with multiple optima. The parameters quantization resolution of input vector xi ( Qi ), generalization size ( g ) and learning rate ( β ) are the decision variables ( xn , n = 1, 2,3 ) of the UGO problem. Therefore, this work develops an advanced DM method that integrates an AIA and a MIMO CMAC NN classifier (AIA-MIMO CMAC NN classifier).
Shows that the purity is not sensitive to the number of clusters for the two methods. However, this is not the same for f-measure. For UMicro, its f-measure has a tendency to decrease when increasing the number of clusters. However, HUE-Stream is not sensitive to the increasing number of clusters because its number of clusters is not fixed but depends on the behavior of data streams. As long as the maximum number of clusters is not exceeded, HUEStream still yields good results. 3 Efficiency Test We evaluate the efficiency of HUE-Stream and UMicro in terms of number of data points proceeded per second and processing time.
I 100 (3) N interval i interval i varying from 1 to N 1 (4) instead of Cost calculation for cost sensitive classifier is performed by using / / (in equation 2). Then, CDE-EM-AVG-N builds N cost sensitive classifiers 18 N. Limsetto and K. Waiyamai W from the original data (train n data) using the obtained cost. Each will hhave different cost based on . Then, it uses these cost sensitive classifiers on nnew data (test data) to obtain estimate e class distributions and / of test data frrom each model.
Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part II by Liang Wu, Yuanchun Zhou, Fei Tan, Fenglei Yang (auth.), Jie Tang, Irwin King, Ling Chen, Jianyong Wang (eds.)