Catalog mining using MIC (Maximal Information Coefficient) of radius end mill tool

Taishi Sakuma, Toshiki Hirogaki, Eiichi Aoyama, Kengo Kubo, Hiroyuki Kodama

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


A novel datamining method has been needed because the loT (Internet of things) is spread over various kinds of industrial fields. We therefore propose to apply a data mining method to tool catalog data-base to improve the manufacturing technologies with machine tools because it is considered to include a useful information derived from tool manufacturing technology as a big-data. In the present report, we look at MIC (Maximal Information Coefficient) as a novel processing method to search for a new knowledge in tool data dala-basc, and construct a hierarchical clustering method based on MIC as a data mining method Comparinga predicting equation derived from the conventional catalog mining method based on a traditional statistics with one based on MIC processing method, we investigate a function of MIC in data mining for end milling conditions. As a result, it can be seen that a constructed method (MIC data mining method) makes it feasible efficiently to find out essential variables in the radius end mill database because a derived practical foimula has less interactions than conventional one with keeping the same prediction accuracy.

Original languageEnglish
Pages (from-to)260-266
Number of pages7
JournalSeimitsu Kogaku Kaishi/Journal of the Japan Society for Precision Engineering
Issue number3
Publication statusPublished - Jan 1 2019


  • Big data
  • Data mining
  • Machine tools
  • MIC (Maximal Information Coefficient)
  • Radius end mill tool

ASJC Scopus subject areas

  • Mechanical Engineering


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