  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.
