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 item set mining with vertical data format has been proposed as an improvement over the basic Apriori which reduces the number of database scans and uses array storage structure. This research paper has proposed a space efficient implementation of finding frequent itemsets with vertical data format using jagged array and reduces the usage of memory by allocating exact memory. An experiment is done between the different implementation of vertical data format approaches viz., array, and jagged array representation. From the experiment it is proved that the proposed jagged array implementation method utilizes the memory effectively when compared with the traditional multidimensional array.

Frequent Pattern Mining, Apriori, FPGrowth, Eclat, Vertical Data Format, Array, and Jagged Array

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