一种高效的高维异常数据挖掘算法

Efficient Data Mining Algorithm for High-dimensional Outlier Data

针对高维异常数据的挖掘问题,提出一种基于最大间隔准则和最小最大概率机的高维异常数据挖掘算法。利用最大间隔准则算法将高维数据投影到低维特征空间,再利用最小最大概率机进行异常数据的挖掘。实验结果表明,该算法检测准确率较高。

To effectively cope with data mining problem for high-dimensional outlier data, a novel outlier data mining algorithm based on Maximum Margin Criterion（MMC） and MiniMax Probability（MMP） machine is proposed. The high dimensional data sets are first projected into lower-dimensional feature space by using MMC algorithm, and MMP machine is adopted to mine outlier data. Experimental results show that the proposed algorithm is feasible and has higher detection accuracy.