Abstract:

A new outlier detection algorithm combining a  rough set and information entropy technology was proposed. This approach could obtain similar outlier sets by means of searching in an attributes subspace, which  could lead the analysis of outlier detection to focus better on narrow and specific object fields. This algorithm divided the original attribute space into several segments, which filtered out those subjects with largest relative entropy negative relative cardinality as the outliers. To prove this algorithm’s effectiveness,  experiments on a  real world dataset were conducted. Theoretical analysis and experimental results showed that this method of outlier detection was efficient and effective.

Key words: attribute deduction, relative entropy, outlier detection