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Rare Association Rule Mining Based on Reinforcement Learning

Published: 28 February 2024 Publication History

Abstract

In some cases, it is more important to discover rare association rules than frequent itemsets. Unique rules represent rare cases, activities or events in real-world applications. Extracting critical activities from massive regular data is crucial. In this study, we introduce RARM-RL, a reinforcement learning-based approach for discovering rare association rules. In RARM-RL, the environment specifies iterative steps to extract rare association rules from the data set. In this method, the agent takes actions to either add or remove an item from the current set of items. After each action, the agent receives a reward from the environment. By repeating this process and trying different actions with varying rewards, the agent is trained to maximize the total reward. The goal is to learn an optimal strategy that allows the agent to form as many rare association rules as possible. Extensive experiments have been conducted to test the effectiveness of this method in mining rare association rules, and the results have shown promising potential for its applicability in different scenarios (such as agent transferability).

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    cover image ACM Other conferences
    ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
    October 2023
    589 pages
    ISBN:9798400707988
    DOI:10.1145/3633637
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 28 February 2024

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    Author Tags

    1. data mining
    2. knowledge discovery
    3. rare association rule mining
    4. reinforcement learning

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