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 conscientiously reviewed and chosen from 191 submissions. The papers hide quite a lot of themes proposing unique learn findings in info mining, spanning purposes, algorithms, software program and platforms, 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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Additional resources for Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part II
An active cluster is cluster that is considered a model of the system. Fading Function In order to capture the clustering characteristics of the data streams which evolve over time, we need to focus on new data more than on old data. To decrease weight of old data over time, a fading function is used. Let λ be the decay rate and t be the elapsed time; the fading function can be defined as follows: 2 (10) Weight of a cluster is the number of data elements in a cluster. Weight is determined according to the fading function.
Among the many data miming (DM) methods that have been developed to solve these classification problems are neural network (NN) and support vector machine (SVM) classifiers. Despite their successful application to classification problems, these classifiers are limited, in that users must use trial-and error to modify specific parameter settings. Fortunately, the setting of the parameters for those classifiers can be viewed as an unconstrained global optimization problem. To overcome this limitation of those classifiers, this work develops an advanced DM method that combines an artificial immune algorithm (AIA) and a MIMO cerebellar model articulation controller NN (CMAC NN) classifier (AIAMIMO CMAC NN classifier).
Tw Abstract. Decision making that involves credit scoring and medical diagnosis can be considered to solve classification problems. Among the many data miming (DM) methods that have been developed to solve these classification problems are neural network (NN) and support vector machine (SVM) classifiers. Despite their successful application to classification problems, these classifiers are limited, in that users must use trial-and error to modify specific parameter settings. Fortunately, the setting of the parameters for those classifiers can be viewed as an unconstrained global optimization problem.
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