Itemset Fusion: A New Technique for Itemsets Generation

Matthew Tunde Ogedengbe, Sahalu B Junaidu, Donfack A Kana

Abstract


Association rule mining derives insight from transactional data but suffers from frequent database scans and explosive generation of candidate sets in a large database. This study presents a new items merging technique called, Itemset Fusion Technique (IFT) to generate unique candidate k-itemsets from fused k-items in transaction dataset. The proposed IFT focused on minimizing database scans and generating optimal number of association rules by creating fused k-itemsets locally within each transaction. The fused k-itemsets created are viable candidate itemsets that exist in the database with non-zero support. The performance of the IFT was compared with the classic Apriori in term of number of database scans and number of association rules generated. Experiments was conducted on two real world datasets show IFT scans the database only once and minimizes association rules generated by over 90% against the standard Apriori algorithm. Thus, the proposed IFT accelerates performance and improves mining productivity significantly.


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