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

Ashkan Sami, Makoto Takahashi, Masaharu Kitamura

Research output: Contribution to conferencePaperpeer-review

Abstract

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
Pages471-475
Number of pages5
Publication statusPublished - 2005
EventSICE Annual Conference 2005 - Okayama, Japan
Duration: 2005 Aug 82005 Aug 10

Other

OtherSICE Annual Conference 2005
Country/TerritoryJapan
CityOkayama
Period05/8/805/8/10

Keywords

  • Data mining
  • Knowledge discovery
  • Rule induction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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