Association Rule Mining Algorithms on Large and Small Datasets: A Comparative Study

Association rule mining (ARM) is widely used approach in data mining to discover trends from databases. There is plethora of ARM algorithms available for frequent itemset mining and generating association rules. This paper makes a comparative study of important ARM algorithms such as Apriori, FP-Growth, LCM and FIN. All these algorithms are applied for large dataset and small dataset, those results are drawn,analysed and compared. Out of them FIN is the most recent algorithm and fast in generating frequent item sets. LCM is an award winning algorithm. FPGrowth generates frequent itemsets without candidate generation approach followed by Apriori. Due to their significant differences, these algorithms are chosen for comparative study. This paper provides the details of the algorithms, illustrations to have deeper insights besides comparing them in terms of performance measures like execution time and efficiency.