Generic Itemset Mining Based on Reinforcement Learning

Generic Itemset Mining Based on Reinforcement Learning
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DOI：
10.1109/access.2022.3141806
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发表时间：
2021-05
期刊：
IEEE Access
影响因子：
3.9
通讯作者：
Kazuma Fujioka;Kimiaki Shirahama
Kazuma Fujioka;Kimiaki Shirahama
中科院分区：
计算机科学3区
文献类型：
--
作者：
Kazuma Fujioka;Kimiaki Shirahama

文献摘要

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项集挖掘中最大的问题之一是每次用户想要提取不同类型的项集时都需要开发数据结构或算法。为了克服这个问题，我们提出了一种称为基于强化学习的通用项集挖掘（GIM-RL）的方法，它提供了一个统一的框架来训练代理来提取任何类型的项集。在 GIM-RL 中，环境制定了从数据集中提取目标类型的项集的迭代步骤。在每个步骤中，代理执行操作以向当前项目集添加或删除项目，然后从环境中获取奖励，该奖励表示该操作产生的项目集与目标类型的相关程度。通过大量的试错步骤，通过不同的动作获得不同的奖励，代理被训练以最大化累积奖励，从而获得最优的动作策略，以形成尽可能多的目标类型的项目集。在这个框架中，只要可以定义适合该类型的奖励，就可以训练用于提取任何类型的项集的代理。在挖掘高效用项集、频繁项集和关联规则方面的大量实验表明了 GIM-RL 的普遍有效性和一项显着的潜力（代理迁移）。我们希望 GIM-RL 为基于学习的项集挖掘开辟一个新的研究方向。
One of the biggest problems in itemset mining is the requirement of developing a data structure or algorithm, every time a user wants to extract a different type of itemsets. To overcome this, we propose a method, called Generic Itemset Mining based on Reinforcement Learning (GIM-RL), that offers a unified framework to train an agent for extracting any type of itemsets. In GIM-RL, the environment formulates iterative steps of extracting a target type of itemsets from a dataset. At each step, an agent performs an action to add or remove an item to or from the current itemset, and then obtains from the environment a reward that represents how relevant the itemset resulting from the action is to the target type. Through numerous trial-and-error steps where various rewards are obtained by diverse actions, the agent is trained to maximise cumulative rewards so that it acquires the optimal action policy for forming as many itemsets of the target type as possible. In this framework, an agent for extracting any type of itemsets can be trained as long as a reward suitable for the type can be defined. The extensive experiments on mining high utility itemsets, frequent itemsets and association rules show the general effectiveness and one remarkable potential (agent transfer) of GIM-RL. We hope that GIM-RL opens a new research direction towards learning-based itemset mining.