Hierarchical Level Based Frequent Pattern Mining

Temporal data mining which considers time attributes, centers on discovering the association of items that occur over a period or discovering items that occur sequentially and frequently. However, there are cases in which an item occurs frequently during a certain period even though it has not occurred frequently during the entire period. In this paper, we propose a TD-HTIP (Temporal Data-Hierarchical Time Interval Period) algorithm that searches for items that occur frequently within a specific time range by constructing a hierarchical time interval period that considers a certain time interval for temporal data. The proposed algorithm constructs a matrix of transaction information containing items that occur over time, and searches for frequent items based on the matrix. Experimental results show that the proposed algorithm enables searching more frequent items compared to the existing algorithms for execution time.