Mining high occupancy patterns to analyze incremental data in intelligent systems

High occupancy pattern mining has been recently studied as an improved method for frequent pattern mining. It considers the proportion of each pattern in the transactions where the pattern occurred. The results of high occupancy pattern mining can be employed for automated control systems in order to make decisions. Meanwhile, the features of the databases have changed, because information technology has advanced. In real-world databases, new transactions are inserted in real time. However, the state-of-the-art approach to high occupancy pattern mining cannot handle incremental databases. Moreover, the existing method also requires a large amount of memory space, because it adopted a BFS-based search in order to find patterns. In this paper, we propose an approach, which is called HOMI (High Occupancy pattern Mining on Incremental databases), that uses a DFS-based search in order to detect patterns, and it mines high occupancy patterns on incremental databases. The performance analysis for both real and synthetic datasets indicates that HOMI has better performance than the state-of-the-art approaches and related algorithms.