Frequent positive and negative (FPN) itemset approach for outlier detection

Abstract

The outlier detection has many applications and research attention to it is increasing. The detection rate is a significant measure for outlier mining that evaluates the outlier detection algorithms' performance. The problem is especially challenging because of the difficulty of defining a significant outlier measure in order to have a better detection rate. This paper proposes a novel approach for outlier detection with consideration of frequent negative itemset. This approach also produces positive itemset together with negative itemset. The knowledge and interesting pattern generated from frequent positive and negative (FPN) itemsets confidently enhances the outlier detection task in this method. The FPN itemset helps identification of transactions that are rare and in conflict with each other. However, discovering negative itemsets remains a challenge. To further investigate the potential knowledge of frequent negative itemsets in outlier detection, an experiment is conducted using the UCI datasets. The FPN itemset approach obtains better detection rate compared to other algorithms for majority datasets, indicating that the proposed approach is a promising approach in solving outlier detection problems.