Mining massively incomplete data sets by conceptual reconstruction
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A Modified Chi2 Algorithm for Discretization
IEEE Transactions on Knowledge and Data Engineering
Issues of agent-based distributed data mining
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
The communicative multiagent team decision problem: analyzing teamwork theories and models
Journal of Artificial Intelligence Research
Multi-knowledge extraction and application
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
The application of intelligent agent technology to simulation
Mathematical and Computer Modelling: An International Journal
A multi-agent decision-theoretic rough set model
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Modelling Multi-agent Three-way Decisions with Decision-theoretic Rough Sets
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
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The key problem in knowledge acquisition algorithm is how to deal with large-scale datasets and extract small number of compact rules. In recent years, several approaches to distributed data mining have been developed, but only a few of them benefit rough set based knowledge acquisition methods. This paper is intended to combine multiagent technology into rough set based knowledge acquisition method.We briefly review the multi-knowledge acquisition algorithm, and propose a novel approach of distributed multi-knowledge acquisition method. Information system is decomposed into sub-systems by independent partition attribute set. Agent based knowledge acquisition tasks depend on universes of sub-systems, and the agent-oriented implementation is discussed. The main advantage of the method is that it is efficient on large-scale datasets and avoids generating excessive rules. Finally, the capabilities of our method are demonstrated on several datasets and results show that rules acquired are compact, having classification accuracy comparable to state-of-the-art methods.