Acceleration of association‐rule based markov decision processes

Abstract

In this paper, we present a new approach for the estimation of Markov decision processes based on efficient association rule mining techniques such as Apriori. For the fastest solution of the resulting association‐rule based Markov decision process, several accelerating procedures such as asynchronous updates and prioritization using a static ordering have been applied. A new criterion for state reordering in decreasing order of maximum reward is also compared with a modified topological reordering algorithm. Experimental results obtained on a finite state and action‐space stochastic shortest path problem demonstrate the feasibility of the new approach.

A regression-based algorithm for frequent itemsets mining

“…Therefore, some studies combine FIM algorithms with various predictive models to predict data. For example, the Markov chain is used in the classical Apriori algorithm for decision advising (Garcia-Hernandez et al , 2009). In material science research, a game-of-life algorithm is used to convert text data into structured data for predicting the microstructure of the alloy (Varde et al , 2004).…”