New data structure and algorithm for Mining Dynamic Periodic Patterns

摘要

Periodic pattern mining searches useful periodic patterns in time-related datasets. Previous studies mostly concern the synchronous periodic patterns. Since static transaction database cannot provide the dynamic and timely information to obtain timely mining results, this study proposes the Dynamic Periodic Pattern Mining model for progressive databases. This study also presents a novel periodic pattern two-dimensional linked list structure to assemble the information of periodic patterns. For each event, the proposed DOEOP algorithm discovers all periodic 1-patterns for all windows of interest (WOI). Finally, the DPPM algorithm can effectively generates all periodic patterns with respect to different WOIs.