Optimized High-Utility Itemsets Mining for Effective Association Mining Paper

K Rajendra Prasad


Association rule mining is intently used for determining the frequent itemsets of transactional database; however, it is needed to consider the utility of itemsets in market behavioral applications. Apriori or FP-growth methods generate the association rules without utility factor of items. High-utility itemset mining (HUIM) is a well-known method that effectively determines the itemsets based on high-utility value and the resulting itemsets are known as high-utility itemsets. Fastest high-utility mining method (FHM) is an enhanced version of HUIM. FHM reduces the number of join operations during itemsets generation, so it is faster than HUIM. For large datasets, both methods are very expenisve. Proposed method addressed this issue by building pruning based utility co-occurrence structure (PEUCS) for elimatination of low-profit itemsets, thus, obviously it process only optimal number of high-utility itemsets, so it is called as optimal FHM (OFHM). Experimental results show that OFHM takes less computational runtime, therefore it is more efficient when compared to other existing methods for benchmarked large datasets.


association rule mining, FHM, frequent itemsets, high-utility itemsets, HUIM,

Full Text:


DOI: http://doi.org/10.11591/ijece.v7i5.pp2911-2918

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).