Towards a new mechanism of extracting cyclic association rules based on partition aspect

Never before a such abundant volume of data is collected as the one which we attend nowadays. Thus, its exploration becomes increasingly difficult, especially if we highlight the temporal aspect during the extraction of association rules. Therefore, several works were devoted to this problematic by introducing the temporal association rules mining. In this paper, we focus on cyclic association rules, classified as a category of the temporal association rules. Indeed, this class aims to discover new relationships between items that display regular cyclic variation over time. As a response to the anomalies characterizing the classical approaches addressing this issue, i.e., SEQUENTIAL and INTERLEAVED algorithms, we introduce in this paper, a new algorithm called PCAR ALGORITHM. In fact, the major advantages characterizing our approach consist on its performance and its incremental aspect. The experiments were carried out to prove the robustness and the efficiency of our proposed algorithm vs the pioneering approaches in the same trend.