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Generic Itemset Mining Based on Reinforcement Learning
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AI
DOI：10.1109/ACCESS.2022.3141806.png)
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Abstract
En 中文
One of the biggest problems in itemset mining is the requirement of developing a data structure or algorithm, every time a user wants;
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;
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;
add or remove an item;
or from the current itemset, and then obtains from the environment a reward that represents how relevant the itemset resulting from the action is;
the target type. Through numerous trial-and-error steps where various rewards are obtained by diverse actions, the agent is trained;
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;
wards learning-based itemset mining.
Keywords:
Itemsets
Data mining
Data structures
Upper bound
Reinforcement learning
Runtime
Metaheuristics
Data mining
itemset mining
knowledge discovery
reinforcement learning
Journal
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