Discovery of High Utility Patterns from Retail Database with adding constraints on LBHUP algorithm
DOI:
https://doi.org/10.26438/ijcse/v6i8.843846Keywords:
Association rules (ARs), Association rule mining, frequent patterns, high utility patterns, pattern miningAbstract
Association-rule mining is commonly used mining technique for finding frequent patterns. In real world applications traditional association-rule mining is not appropriate since the purchased item have different factors, for example, amount and benefit. High utility pattern mining was used for unravelling the limitations of the association-rule mining as far as amount and benefit. There are algorithms for finding high utility patterns from static databases. Some researches worked for handling dynamic dataset but huge computational time and multiple database scan was required. In this paper, a system is proposed for finding high utility patterns which uses list based data structure for dynamic dataset. To improve computational time and memory, constraints like item, date or length are used. A few experiments are led to demonstrate the execution of the proposed system with and without using constraints regarding time and memory.
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