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MHEIClosed: An Efficient Algorithm for Mining Closed High-Efficiency Itemsets
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AI
DOI：10.1007/978-981-95-4960-3_28.png)
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Abstract
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High-Efficiency Itemset Mining (HEIM) is an emerging extension of utility-based mining that considers utility and investment, addressing key limitations of High Utility Itemset Mining (HUIM). However, existing HEIM methods often produce large and redundant result sets, complicating interpretation and post-analysis. To address this, we propose Closed High-Efficiency Itemset Mining (CHEIM), which focuses on extracting concise and non-redundant patterns while preserving essential utility and investment information. This paper presents MHEIClosed, a novel algorithm for efficiently mining closed high-efficiency itemsets. MHEIClosed incorporates advanced techniques from closed itemset and HEIM approaches, including pruning strategies based on tighter upper bounds (ssef and slef), transaction merging, and efficient closure checking. Extensive experiments on several real-world datasets demonstrate that MHEIClosed significantly outperforms the state-of-the-art HEPMClosed algorithm in terms of runtime and memory consumption, particularly on dense or large-scale datasets. These results validate MHEIClosed's scalability and effectiveness in mining meaningful patterns, making it well-suited for practical data mining applications.
Keywords:
High-efficiency itemset
closed pattern mining
pruning strategies
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