Mining Significant Statistically Blowout Patterns

One blowout pattern is a zone on multiple data streams in which the data points are dense but highly unbalanced. It can be applied into a variety of fields. for example, it may imply a successful sales promotion especially for a few areas. the difficulties of exploring blowout patterns are: 1). It is not periodic, 2). the distribution is unknown, and 3). How to distinguish it from outliers or clusters. We have proposed a novel method for it. First, we employ a density based clustering algorithm to detect dense data points. Second, we use a novel concept (Normal Standard Deviation: NSD) for the evaluation of the data distribution in the zones. the zones that include dense but unbalanced data points are highlighted as significant blowout patterns. the results demonstrate that our method can find and locate blowout patterns efficiently and effectively compared to kernel framework and slide window.