The Research Of Support Vector Machine Based On Fuzzy Clustering In Classify Algorithm

Being a class of automatic and intelligent data analysis techniques, data mining, also called KDD, which aims at extracting novel and useful knowledge from large volumes of data, has emerged rapidly in recent ten years. Classification is one of the data mining At present, most of existing Classification algorithm is based traditional statistics. But they may not work well in practical case with limited samples and easily lead to the problem of overfitting. Support Vector Machine has become one of rising data mining techniques because of its excellent theory.SVM is a new kind of promising machine learning algorithm proposed by Vapnik and his group at AT&T Bell laboratory. It has become the new research hotspot after the research of Artificial Nerve Net and it will push the development in machine learning theory and technology.In this thesis we firstly overview data mining techniques, making a brief description about the concept, basic model, typical structure, and some popular techniques of data mining. Then we focus on support vector machine and discuss its theory foundation, basic concepts, and crucial techniques of support vector machine. With these background, we further study several generally algorithms about support vector machine.But it still hard to application and dissemination in data mining, the main reason is that the cost of training of support vector machines is too much and the speed of study is too slowly when it process the large datasets. To result this problem, we study the cooperation of support vector machine and clustering method. Through cut some useless samples, the scale of the training data set is reduced greatly and the training speed of SVM is improved enormously. At last, the application of NC-SVM in talents cognition system gets better result.