A fundamental approach to discover closed periodic-frequent patterns in very large temporal databases

Periodic frequent-pattern mining (PFPM) is a vital knowledge discovery technique that identifies periodically occurring patterns in a temporal database. Although traditional PFPM algorithms have many applications, they often produce a large set of periodic-frequent patterns (PFPs) in a database. As a result, analyzing PFPs can be very time-consuming for users. Moreover, a large set of PFPs makes PFPM algorithms less efficient regarding runtime and memory consumption. This paper handles this problem by proposing a novel model of closed periodic-frequent patterns (CPFPs) found in databases. CPFPs are less expensive to mine because they represent a concise and lossless subset uniquely describing the entire set of PFPs. We also present an efficient depth-first search algorithm, called Closed Periodic-Frequent Pattern-Miner (CPFP-Miner), to discover the patterns. The proposed algorithm utilizes the weighted ordering of the patterns concept to reduce the patterns' search space. On the other hand, the current periodicity concept is also applied to prune aperiodic patterns from the search space. Extensive experiments on both real-world and synthetic databases demonstrate that the CPFP-Miner algorithm is efficient. It outperforms the state-of-the-art algorithms regarding runtime requirements, memory consumption, and energy consumption on several real-world and synthetic databases. Additionally, the scalability of the CPFP-Miner algorithm is demonstrated to be more effective and productive than the state-of-the-art algorithms. Finally, we present two case studies to show the functionality of the proposed patterns.