Adaptive SVM decision tree classification algorithm based on bisecting K-means
基于二分K-均值的SVM决策树自适应分类方法

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.