Abstract—In this paper the problem of discovering association rules among items in extremely large databases has been considered. A novel mining algorithm named Improved Cluster Based Association Rules (ICBAR) has been proposed which can explore efficiently the large itemsets. Achieving this and initializing the cluster table (where transaction records with length k are placed in kth cluster table), database will be once scanned. Simultaneously an array with appropriate size for each itemset (named itemset array (IA)) will be created. Here kth element in the array of each itemset indicates number of transaction records in kth cluster table which have that itemset. Presented method not only prunes considerable amounts of data by comparing with the partial cluster tables but also reduces the number of large candidate itemset that must be checked in each cluster through itemset arrays. Performance and efficiency of proposed method has been compared with CBAR and Apriori algorithms. Experiments illustrate that ICBAR will do better than both of them.
Index Terms—Association rule, data mining, cluster table, itemset array.
R. Sheibani is with the Department of Computer, Mashhad Branch, Islamic Azad University, Mashhad, Iran (e-mail: email@example.com)
A. Ebrahimzadeh is with the Sama technical and vocational training college, Islamic Azad University, Mashhad branch, Mashhad, Iran (e-mail: firstname.lastname@example.org).
Cite: Reza Sheibani and Amir Ebrahimzadeh, "ICBAR: An Efficient Mining of Association Rules in Huge Databases," International Journal of Computer Theory and Engineering vol. 4, no. 5, pp. 798-801, 2012.