Online analytical mining association rules using Chi-square test
International Journal of Business Intelligence and Data Mining
An approach to mining bundled commodities
Knowledge-Based Systems
Online mining of fuzzy multidimensional weighted association rules
Applied Intelligence
Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Agent-mining interaction: an emerging area
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
Techniques for finding similarity knowledge in OLAP reports
Expert Systems with Applications: An International Journal
Discovering diverse association rules from multidimensional schema
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Mining sequential patterns with extensible knowledge representation
Intelligent Data Analysis
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Multiagent systems and data mining have recently attracted considerable attention in the field of computing. Reinforcement learning is the most commonly used learning process for multiagent systems. However, it still has some drawbacks, including modeling other learning agents present in the domain as part of the state of the environment, and some states are experienced much less than others, or some state-action pairs are never visited during the learning phase. Further, before completing the learning process, an agent cannot exhibit a certain behavior in some states that may be experienced sufficiently. In this study, we propose a novel multiagent learning approach to handle these problems. Our approach is based on utilizing the mining process for modular cooperative learning systems. It incorporates fuzziness and online analytical processing (OLAP) based mining to effectively process the information reported by agents. First, we describe a fuzzy data cube OLAP architecture which facilitates effective storage and processing of the state information reported by agents. This way, the action of the other agent, not even in the visual environment of the agent under consideration, can simply be predicted by extracting online association rules, a well-known data mining technique, from the constructed data cube. Second, we present a new action selection model, which is also based on association rules mining. Finally, we generalize not sufficiently experienced states, by mining multilevel association rules from the proposed fuzzy data cube. Experimental results obtained on two different versions of a well-known pursuit domain show the robustness and effectiveness of the proposed fuzzy OLAP mining based modular learning approach. Finally, we tested the scalability of the approach presented in this paper and compared it with our previous work on modular-fuzzy Q-learning and ordinary Q-learning.