Mining Rare Itemset with Automated Support Thresholds

Abstract

Problem statement: Frequent itemset mining is an important task in data mining to discover the hidden, interesting associations between items in the database based on the user-specified support and confidence thresholds. Approach: In order to find important associations, an appropriate support threshold has to be specified. The support threshold plays a key role in deciding the interesting itemsets. The rare itemsets may not found if a high threshold is set. Some uninteresting itemsets may appear if a low threshold is set. Results: This study proposes an approach to obtain the frequent itemsets involving rare items by setting the support thresholds automatically. Experimental results show that this approach produces rare and frequent itemsets in sparse and dense datasets. According to T20I6D100K, 97.76% of the FIs are generators wherein Mushrooms 1.38% of the FIs are the generators. Conclusion: The proposed algorithm produces both frequent and rare itemsets in an effective way. In future, computational efforts can still be reduced by implementing the algorithm as parallel algorithm.