%0 Journal Article
%T Adaptive SVM decision tree classification algorithm based on bisecting K-means<br>基于二分K-均值的SVM决策树自适应分类方法
%A QIU Guo-yong
%A ZHANG Jiao
%A <br>裘国永
%A 张　娇
%J 计算机应用研究
%D 2012
%I 
%X This paper analyzed and researched the applications of adaptive dimension reduction algorithm in high-dimensional data mining. To improve the situation of low accuracy and low clustering quality caused by existing data mining algorithms dealing with high dimensional data, it proposed an adaptively classification algorithm, combining bisecting K-means clustering and support vector machine decision tree, for high dimensional data classification. The BKM-SVMDT algorithm transformed the high dimensional dataset into low dimensional one to ensure data mining in the low-dimensional space, and its results could in turn help SVMDT in high-dimensional space. Adaptively executed the algorithm in order to obtain better classification accuracy and efficiency. Extensive experimental results on standard datasets show the effectiveness of the algorithm.
%K bisecting K-means（BKM）
%K SVM decision tree（SVMDT）
%K dimension reduction
%K adaptive algorithm<br>二分K-均值
%K 支持向量机决策树
%K 降维
%K 自适应算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=38AAB79AD8A56B53123311C549A9F151&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=F3090AE9B60B7ED1&sid=5A4A99FBBB7FDB1B&eid=769DA7877E6F8E5C&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=9