• Harsh Vasani
• Ashwin Raiyani
• Ravirajsinh Vaghela

High-utility itemset mining (HUIM) is an important data mining task with broad applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces a number of new ideas to more efficiently discovers high-utility itemsets both in terms of execution time and memory. EFIM trust on two upper-bounds named sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper-bounds in linear time and space. Moreover, to decreases the cost of database scans, EFIM come up with efficient database projection and trans- action merging techniques. An extensive experimental study on various datasets appears that EFIM is in general two to three orders of magnitude faster and consumes up to eight times fever memory.

High-Utiity mining, itemset mining, pattern mining

"Improve EFIM Algorithm to Efficiently Mine High Utility Itemset", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.20-31, May-2019, Available :http://www.jetir.org/papers/JETIR1905R04.pdf

"Improve EFIM Algorithm to Efficiently Mine High Utility Itemset", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp20-31, May-2019, Available at : http://www.jetir.org/papers/JETIR1905R04.pdf