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Discovery of closed high utility itemsets using a fast nature-inspired ant colony algorithm

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Abstract

Mining high utility itemset (HUIM) from an extensive database is a crucial descriptive task in data mining, which considers both the quantity and unit profit factor in revealing the ultimately profitable items. However, it may discover a vast number of HUIs which can be challenging to interpret by a user and also reduce the efficiency of the mining process. A solution to this problem is to mine a Closed high utility itemset, a more compact and lossless form of HUIs. In this paper, a fast nature-inspired meta-heuristic approach CHUI-AC (Closed high utility itemset mining using ant colony algorithm) has been introduced to mine CHUIs. This is the first work on mining CHUI using a nature-inspired ant colony algorithm. CHUI-AC maps the feasible solution space to a directed graph with quadratic space complexity to guide the searching efficiently. Several experiments on real-world datasets show that the proposed algorithm outrun the state-of-the-art algorithms in terms of execution time and rate of convergence. Moreover, the scalability experiments demonstrate that CHUI-AC is linearly scalable with respect to the number of transaction and number of items.

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Notes

  1. The code can be found at https://github.com/chuim-ac/CHUI-AC

  2. The codes for other compared models can be found at https://github.com/chuim-ac/SPMF

  3. The dataset can be found at https://www.kaggle.com/irfanasrullah/groceries

  4. This dataset can be found at https://www.kaggle.com/sulmansarwar/transactions-from-a-bakery

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Acknowledgements

This study has been supported by Indian Institute of Technology, Kharagpur, India.

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  1. Department of Mathematics, Indian Institute of Technology, Kharagpur, 721302, India

    Subhadip Pramanik & Adrijit Goswami

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  1. Subhadip Pramanik
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Correspondence to Subhadip Pramanik.

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Pramanik, S., Goswami, A. Discovery of closed high utility itemsets using a fast nature-inspired ant colony algorithm. Appl Intell 52, 8839–8855 (2022). https://doi.org/10.1007/s10489-021-02922-1

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