Abstract
Existing association-based outlier detection approaches were proposed to seek for potential outliers from huge full set of uncertain data streams ($UDS$), but could not effectively process the small scale of $UDS$ that satisfies preset constraints; thus, they were time consuming. To solve this problem, this paper proposes a novel minimal rare pattern-based outlier detection approach, namely Constrained Minimal Rare Pattern-based Outlier Detection (CMRP-OD), to discover outliers from small sets of $UDS$ that satisfy the user-preset succinct or convertible monotonic constraints. First, two concepts of ‘maximal probability’ and ‘support cap’ are proposed to compress the scale of extensible patterns, and then the matrix is designed to store the information of each valid pattern to reduce the scanning times of $UDS$, thus decreasing the time consumption. Second, more factors that can influence the determination of outlier are considered in the design of deviation indices, thus increasing the detection accuracy. Extensive experiments show that compared with the state-of-the-art approaches, CMRP-OD approach has at least 10% improvement on detection accuracy, and its time cost is also almost reduced half.
Efficiently Predicting Frequent Patterns over Uncertain Data Streams
