Association rules based algorithm for identifying outlier transactions in data stream

Most outlier detection algorithms are proposed to discover outlier patterns from static databases. Those algorithms are infeasible for instant identification of outlier patterns in data streams that continuously arriving and unbounded data serve as the data sources in many applications such as sensor data feeding. In this paper an association rules based method is proposed to find outlier patterns in data streams. The presented work segments transactions from data streams and then finds approximate frequent itemsets with single data scan instead of requiring multiple scans. Based on the derived association rules some transaction can be identified as outliers if their outlier degrees are higher than a predefined threshold. The proposed method not only just finds the outlier patterns but also identifies the most possible items that induce the abnormal transactions in the data streams. Efficiency comparisons with frequent itemsets-based work are also done to verify the effectiveness of the proposed framework.