TKU-BChOA: an accurate meta-heuristic method to mine Top-k high utility itemsets

High utility itemset mining is an essential new task in data mining, which is obtained from the extension of frequent itemset mining problems. The difference between these two groups is that in high utility itemset mining, the utilities of the items are essential in addition to the number of items....

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Bibliographic Details
Published in:The Journal of supercomputing 2024-09, Vol.80 (14), p.21284-21305
Main Authors: Mofid, Amir Hossein, Daneshpour, Negin, Torabi, Zeinab, Taghavi, Parvin
Format: Article
Language:English
Subjects:
Algorithms
Compilers
Computer Science
Data mining
Datasets
Heuristic
Heuristic methods
Interpreters
Processor Architectures
Programming Languages
Utilities
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Summary:High utility itemset mining is an essential new task in data mining, which is obtained from the extension of frequent itemset mining problems. The difference between these two groups is that in high utility itemset mining, the utilities of the items are essential in addition to the number of items. To find the itemsets with more utilities, defining a minimum utility threshold is necessary by testing different solutions to obtain the desired outcome. This is challenging and needs more time to reach the appropriate value. In this article, we propose a method called TKU-BChOA to explore top-k high utility itemsets according to the number of required itemsets based on the Chimp meta-heuristic algorithm. Because, in comparison with the meta-heuristic methods, the deterministic methods require more time and memory. Experimental results on five datasets show that the proposed approach outperforms the previous deterministic methods in memory usage. The proposed method performs better in terms of time and memory on large datasets than other approximate methods and is more accurate.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-024-06275-7