FMIC: Finding Most Important Contributors in small companies with diverse projects

Ashkan Sami, Makoto Takahashi, Masaharu Kitamura

Research output: Contribution to conferencePaperpeer-review


This paper presents an approach to find the most important combinations in problems that few members perform several tasks or are in several positions that results in determined number of outcomes. The position of the members regarding to a specific origin must be an important factor. FMIC deploys a special mapping that requires only addition and then based on algorithm similar to basket analysis finds the most important combinations. Speed is the main advantage of the approach. The database is scanned only once. In addition to artificially generated data, FMIC was applied to some DNA data and the algorithm found all the patterns with different support.

Original languageEnglish
Number of pages5
Publication statusPublished - 2005
EventSICE Annual Conference 2005 - Okayama, Japan
Duration: 2005 Aug 82005 Aug 10


ConferenceSICE Annual Conference 2005


  • Data mining
  • Knowledge discovery
  • Rule induction


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