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HAUOPM: High Average Utility Occupancy Pattern Mining

  • Research Article-Computer Engineering and Computer Science
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Abstract

Considerable research has been dedicated to the area of data mining. Among the techniques used for this purpose, frequent pattern mining focuses on revealing patterns that occur frequently in the data. The field of data mining has expanded to include the extraction of patterns that can be beneficial to organizations, known as high-utility pattern mining. To improve the quality of these patterns, occupancy has been introduced as an interestingness measure in recent research. As a result, a new approach known as high-utility occupancy pattern mining (HUOPM) has been developed to identify patterns that make a significant contribution to their supporting transactions, as determined by a utility occupancy metric. A high-utility occupancy pattern is defined as a pattern whose utility occupancy exceeds a threshold value specified by the user. In the realm of transactional databases, the conventional definition of utility occupancy for an itemset involves adding up the utility occupancies for each of its supporting transactions. However, a key drawback of the traditional approach to HUOPM is that it does not account for the length of an itemset. Consequently, some items within an itemset may have higher utility occupancies than others, and the resulting representation may not accurately reflect the relative importance of each item within the itemset. To overcome this limitation, a novel high average utility occupancy pattern mining (HAUOPM) algorithm has been proposed. This algorithm uses a novel measure called average utility occupancy to mine high average utility occupancy patterns. As HAUOPM is not anti-monotonic, a novel average utility occupancy upper bound measure is used to prune unpromising itemsets. It can be observed that when two itemsets are compared, utility of one itemset might be greater than the other, whereas its average utility occupancy is less than the other itemset. Therefore, it is possible that some high-quality patterns are not discovered. To overcome this limitation, we introduce a novel measure called transaction utility occupancy to discover high-quality patterns. A novel average utility occupancy tree structure is used to enumerate the search space. This research uses a modified average utility occupancy list structure to store information about items/itemsets. Extensive experimentation has been performed on real and synthetic datasets to the test the effectiveness of the proposed HAUOPM algorithm.

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  1. Computer Engineering, Sardar Vallabhbhai National Institute of Technology, Keval Chowk, Surat, Gujarat, 395007, India

    Mathe John Kenny Kumar & Dipti Rana

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  1. Mathe John Kenny Kumar
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  2. Dipti Rana
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Correspondence to Mathe John Kenny Kumar.

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Kumar, M.J.K., Rana, D. HAUOPM: High Average Utility Occupancy Pattern Mining. Arab J Sci Eng 49, 3397–3416 (2024). https://doi.org/10.1007/s13369-023-07971-x

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