An Empirical Comparison and Effect of Clustering Massive Data on Association Rule Mining - Saha, Sanjib (International Journal of Computer Sciences and Engineering, 2026)
This paper explores the different techniques of association rule mining (ARM) and clustering in unsupervised learning and data mining. As many works have already been done on the Apriori algorithm of ARM, but there was very limited work on the other algorithms such as Predictive Apriori, Tertius and Filtered Associator. The main problem of ARM is handling a large dataset and then scanning it repeatedly. A pre-clustering effort would reduce the dataset size for each such scan for each such cluster and thus would offer overall less time requirement. The different algorithms of ARM are executed on two different datasets such as Breast Cancer and Zoo. There is a scope for improvement in performance by applying filters and clustering techniques on ARM. The best model has been proposed as follows: (i) Use data source; (ii) Apply filters (numeric to nominal and replace missing value); (iii) Apply additional filters (attribute selection or merge two values or remove folds) or evaluation method (training set maker); (iv) Apply clustering methods (K-Means, Farthest Fast, Expectation Maximization, Hierarchical and Make Density Based); (v) Apply ARM methods (Apriori, Predictive Apriori, Tertius and Filtered Associator); (vi) View result. The different ARM algorithms are evaluated with certain metrics and compared against each other based on accuracy, lift value and execution time. However, the best rules found from each ARM algorithm are different. The paper discusses the effect of clustering on ARM and claims that clustering the data before applying ARM is better