UWFP-Outlier: an efficient frequent-pattern-based outlier detection method for uncertain weighted data streams

Abstract

In this paper, we propose an efficient frequent-pattern-based outlier detection method, namely, UWFP-Outlier, for identifying the implicit outliers from uncertain weighted data streams. For reducing the time cost of the UWFP-Outlier method, in the weighted frequent pattern mining phase, we introduce the concepts of the maximal weight and maximal probability to form a compact anti-monotonic property, thereby reducing the scale of potential extensible patterns. For accurately detecting the outliers, in the outlier detection phase, we design two deviation indices to measure the deviation degree of each transaction in the uncertain weighted data streams by considering more factors that may influence its deviation degree; then, the transactions which have large deviation degrees are judged as outliers. The experimental results indicate that the proposed UWFP-Outlier method can accurately detect the outliers from uncertain weighted data streams with a lower time cost.