A Parallel Algorithm to Mine Abnormal Patterns from Satellite Data

Mining abnormal patterns is important in many areas. With the prevalence of big data, in order to ensure efficiency, an algorithm named PPSpan (JOMP-based parallel Prefix Span) is proposed under the research of traditional serial sequential pattern mining methods. Firstly, redundant parameters are eliminated with grey correlation analysis. Secondly, outlier information is extracted according to the corresponding parameter threshold and each parameter is discretized with information entropy. Finally, PPSpan algorithm is employed to mine patterns. The algorithm can effectively mined the abnormal patterns from big dataset. Moreover, we verify the feasibility and effectiveness of the proposed method through an experimental analysis of a certain satellite data.