Significant Itemset Mining with Support-Attractiveness Framework

Support based popular itemset mining often misguides the users in proper knowledge discovery in connection with many real world applications such as social network analysis, source identification of epidemic diseases etc. Support only expresses the frequency of the itemsets in the database. In reality, support-based pruning technique does not bother about the significance of the itemsets in the context of correlation or associability. Therefore, support-based pruning technique often ignores valuable itemsets. In addition, support-based pruning technique sometimes generates commonsense itemsets that has lack of unexpectedness. Furthermore, it promotes isolated items that have no utilization in association rule mining. To fill up the shortcomings of support-based pruning technique, this paper first introduces the concept of attractiveness of the itemsets and thereafter, develops an efficient support-attractiveness framework (SAF) for itemset pruning. An efficient algorithm called SAFApriori (Support-Attractiveness Framework based Apriori) is proposed for significant itemset mining based on the SAF. The proposed algorithm has been tested with real and synthetic datasets and the obtained results show the effectiveness of the proposed algorithm.