Building plant quake-proof information inference system based on hybrid data mining approach

Shu, Y.; Nakajima, Norihiro

Proceedings of 1st International Workshop on Risk Management System with Intelligent Data Analysis (RMDA 2005) in Conjunction with 19th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI 2005), p.35 - 44, 2005/06

This paper presents an intelligent information inference system based on a hybrid data mining approach, which integrates human cognitive model in a data mining loop. In the proposed system, the mining control agent emulated human analysts interacts directly with the data miner, analyzing and verifying the output of the data miner and controlling the data mining process. In additional, the neural network method, which is adopted as a core component of the proposed hybrid data mining method, is evolved by adding the retraining facility and explaining function for handling complicated quake-proof data of nuclear power plant. To demonstrate how the method can be used as a powerful tool for extracting information relevant to plant safety and reliability, plant quake-proof testing data have been applied to the inference system.