An Outlier Mining Algorithm Based on Attribute Entropy

This paper describes the outlier data mining and commonly used outlier mining methods, on this basis, it proposes an outlier mining algorithm based on attribute entropy (OMABAE). Firstly, the concept of attribute entropy is introduced to calculate attribute entropy of each attribute, and constructs the attribute entropy matrix. Secondly, the object entropy of each object is computed according to the attribute entropy matrix, and finally outlier will be detected by comparing the object deviation degree with entropy threshold. The experimental results show that this algorithm can detect outlier efficiently.