Patterned Growth algorithm using Hub-Averaging without pre-assigned weights

The concept of finding frequent itemsets without pre-assigned weights is of great importance in Association Rule Mining (ARM). The prime advantage of this approach is that weights can be derived from the dataset itself rather than being given by domain expert. The modification of Apriori algorithm for Weighted Association Rule Mining (WARM) without pre-assigned weights using HITS algorithm has been attempted in the past. However, drift effect is a major limitation of HITS algorithm. In this paper, a new approach HAP-Growth (Hub-Averaging Pattern-Growth) has been proposed for WARM without pre-assigned weights. HAP-Growth algorithm generates frequent itemsets using Hub-Averaging in conjunction with pattern tree approach. Performance of the proposed algorithm has been compared with HITS algorithm in conjunction with pattern tree approach and the existing algorithm. Experimental studies have been carried out on large number of synthetic datasets of varying sizes (generated using IBM Synthetic Data Generator) and real life datasets (taken from UCI Machine Learning Repository and other sources). It is observed that for large datasets, there is drastic reduction in the computational time for the proposed algorithm and at the same time drift effect is reduced to a great extent.