Various Research Opportunities in High Utility Itemset Mining
Sandeep Dalal1, Vandna Dahiya2
1Sandeep Dalal, Assistant Professor, Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, India.
2Vandna Dahiya, Phd Research Scholar, Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 2455-2461 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7213118419/2019©BEIESP | DOI: 10.35940/ijrte.D7213.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Pattern mining is a technique, which discovers interesting, hidden, unpredicted and useful patterns of data from the database. Most of the research work in pattern mining has been focused on the traditional way of Frequent Itemset Mining (FIM) and Association Rule Mining (ARM) for pattern-discovery. Patterns in frequent itemset mining are based on the occurrence frequency of items. Although frequent pattern mining is useful, the assumption that ‘frequent patterns are interesting,’ doesn’t hold for numerous applications. High Utility Itemset Mining (UIM) overcomes this limitation of frequent itemset mining. The aim of HUIM is to find the patterns based on a utility function where the utility can be measured in terms of revenue, profit, weight, frequency, interestingness or time spent on some webpage, etc. Mining patterns with high utility can be seen as a generalization of FIM where the transaction database is the input and every item is having a utility factor representing its importance and might have non-binary quantities in the transactions. This paper surveys various recent advances and research opportunities in the field of high utility itemset mining.
Keywords: Itemset Mining, High Utility, Frequent Itemset, Data Mining, Candidate Pruning..
Scope of the Article: Data Mining.