Eclat_RPGrowth: Finding Rare Patterns Using Vertical Mining and Rare Pattern Tree

Frequent pattern mining is one of the key research areas in the Data Mining (DM) paradigm. There are many algorithms in the literature to identify the frequent itemsets whereas research on rare pattern mining is in the burgeoning stage. Rare items are the infrequent items, where few applications like medical diagnosis, telecommunications, and false alarm detection in industries demand for rare patterns and rare associations with frequent or infrequent items sets in the database. The algorithms that are used to identify frequent items can also be used to identify rare patterns. However, such algorithms suffer from RareItemProblem. Rare Pattern Mining algorithms that are based on Apriori and FP-Growth were designed but Eclat-based rare pattern mining algorithms have not been explored. This paper proposes an Eclat-RPGrowth, algorithm to find rare patterns and the support of itemset is calculated by using intersection of BitSets for corresponding k − 1 itemsets. Also, this research work proposes a variant of Eclat_RPGrowth as Eclat_PRPgrowth. Both the algorithms are outperformed in execution time, and with the number of rare items generated.