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.