一种基于信息熵的异常数据挖掘算法

信息熵是粒计算理论中度量不确定信息的重要工具之一, 已有的异常数据挖掘算法主要针对确定性的异常
数据挖掘, 采用信息熵度量不确定性数据进行异常数据挖掘的研究报道较少. 鉴于此, 在引入信息熵概念的基础上,
定义基于信息熵的异常度来度量数据之间的异常程度, 并提出基于信息熵的异常数据挖掘算法, 该算法可有效进行
异常数据的挖掘. 理论分析与实验结果表明, 所提出算法是有效可行的.

A kind of outlier mining algorithm based on information entropy

Information entropy is one of the important tool to measure the uncertainty information in the information theory.
Many existing algorithms of outlier mining mainly aim at certainty data, and little work has been done for the uncertainty
data aiming to outlier mining based on the information entropy. Therefore, after introducing information entropy concept,
outlier degree based on information entropy is defined for measuring the outlier data. Furthermore, an algorithm for outlier
mining based on information entropy is proposed, which can effectively obtain outliers from data set. Finally, theoretical
analysis and experimental results show that the algorithm is efficient and feasible.