基于Fuzzy理论的数据挖掘算法研究Ⅱ

The Research on Algorithms for Data Mining Based on Fuzzy Theory

"数据挖掘"是数据处理的一个新领域.支持向量机是数据挖掘的一种新方法,该技术在很多领域得到了成功的应用.但是,支持向量机目前还存在许多局限,当支持向量机的训练集中含有模糊信息时,支持向量机将无能为力.为解决一般情况下支持向量机中含有模糊信息(模糊参数)问题,研究了模糊机会约束规划、模糊分类中的模糊特征及其表示方法,建立了模糊支持向量分类机理论,给出了模糊线性可分的模糊支持向量分类机算法.

Data Mining is a new filed in data processing research. Support Vector Machine （SVM） is one of the new methods using in data mining, which has gained great applicable success. However, there are still plenty of limitations in SVM. For example, SVM won＇t work if its training set contains uncertain information. In order to solve the problem presented above, this article discusses the constraining programming of uncertain chance and the characteristic of uncertain classification as well as its expression methods. The algorithm for classifying Support Vector Machine is also included in this article.