 An approach for high utility pattern miningMalaya Dutta Borah and Rajni Jindal International Journal of Data Analysis Techniques and Strategies, 2018, vol. 10, issue 2, 124-152 Abstract: Mining high utility pattern has become prominent as it provides semantic significance (utility/weighted patterns) associated with items in a transaction. Data analysis and respective strategies for mining high utility patterns is important in real world scenarios. Recent researches focused on high utility pattern mining using tree-based data structure which suffers greater computation time, since they generate multiple tree branches. To cope up with these problems, this work proposes a novel binary tree-based data structure with average maximum utility (AvgMU) and mining algorithm to mine high utility patterns from incremental data which reduces tree constructions and computation time. The proposed algorithms are implemented using synthetic, real datasets and compared with state-of-the-art tree-based algorithms. Experimental results show that the proposed work has better performance in terms of running time, scalability and memory consumption than the other algorithms compared in this research work. Keywords: high utility pattern mining; frequent pattern mining; tree-based data structure; incremental mining; data analysis; average maximum utility; AvgMU. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:10:y:2018:i:2:p:124-152 Access Statistics for this article More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd |
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