Extraction operation know-how from historical operation data - using characterization method of time series data and data mining method

Kazuhiro Takeda, Yoshifumu Tsuge, Hisayoshi Matsuyama

Research output: Contribution to journalArticle


In these days, it is very difficult to hand down experts' operation know-how to beginner, because of operation technique of a large and highly complex plant and reducing operators. On the other hand, data mining methods (See5, naive bayes, k-nearest neighbor, and so on) has been proposed as knowledge discovering methods from a huge amount of data. See5 outputs decision trees or IF-THEN rules as data mining results. However, See5 cannot recognize data as time series. In this study, an extraction method of experts' operation know-how from historical operation data is proposed. Furthermore efficiencies of the proposed method are demonstrated by numerical experiments using a dynamic simulator.

Original languageEnglish
Pages (from-to)375-381
Number of pages7
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - Dec 1 2004
Externally publishedYes


All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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