A Memory Efficient Implementation of Frequent Itemset Mining with Vertical Data Format Approach - Sumathi, P; Murugan, Dr. S (International Journal of Computer Sciences and Engineering, 2025)
Data mining is the process of extracting the concealed information and rules from large databases. But the real world datasets are sparse, dirt and also contain hundreds of items. Frequent Pattern Mining (FPM) is one of the most intensive problems in discovering frequent itemsets from such datasets. Apriori is one of the premier and classical data mining algorithms for finding frequent patterns but it is not an optimized one. So over last two decades a remarkable variations and improvements were made to overcome the inefficiencies of Apriori algorithm such as FPGrowth, TreeProjection, Charm, LCM, Eclat and Direct Hashing and Pruning (DHP), RARM, ASPMS etc., In any case, a little enhancement in the algorithm improves the mining process considerably. Frequent itemset mining with vertical data format approach has been proposed as an improvement over the basic Apriori, which reduces the number of database scans and also uses array storage structure. This research paper has proposed a space efficient implementation of finding frequent itemsets with vertical data format using jagged array. It reduces the usage of memory by allocating exact memory. An experiment is done between the array implementation of vertical data format approach and jagged array implementation. From the experiment it is proved that the proposed jagged array implementation method utilizes the memory efficiently when compared with the traditional multidimensional array