Published Paper Details:

Frequent itemset mining is important task in data mining domain. This is applicable in variety of applications such as market-basket analysis, browsing history analysis, transaction record analysis, etc. Lot of work has been done in the domain of co-occurrence pattern extraction and association rule mining. The existing system works on static dataset as an input. The proposed system focuses on temporal analysis of data and extracts co-occurrence patterns from dataset based on timestamp information. Each record in a dataset has it is own time information. Based on the time information data is sliced in 3 dimensional cube. The apriori algorithm is extended to work with time cube information data. Using time interval cubes, the co-occurrence of pattern is analyzed periodically and with certain time interval. For processing multiple time cube simultaneously, a multithreaded environment is proposed to improve system efficiency. To solve the overestimation problem, a density threshold value is checked for each time cube. The performance of system is tested on various dataset and its execution time and memory is compared with the existing approach.