New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports

The problem of mining partial periodic patterns is an important issue with many applications. Previous studies to find these patterns encounter efficiency and effectiveness problem. The efficiency problem is that most previous methods were proposed to find frequent partial periodic patterns by extending the well-known Apriori-like algorithm. However, these methods generate many candidate partial periodic patterns to calculate the patterns’ supports, spending much time for discovering patterns. The effective problem is that only one minimum support threshold is set to find frequent partial periodic patterns but the results is not practical for real-world. In real-life circumstances, some rare or specific events may occur with lower frequencies but their occurrences may offer some vital information to be referred in decision making. If the minimum support is set too high, the associations between events along with higher and lower frequencies cannot be evaluated so that significant knowledge will be ignored. In this study, an algorithm to overcome these two problems has been proposed to generating redundant candidate patterns and setting only one minimum support threshold. The algorithm greatly improves the efficiency and effectiveness. First, it eliminates the need to generate numerous candidate partial periodic patterns thus reducing database scanning. Second, the minimum support threshold of each event can be specified based in its real-life occurring frequency.