Comparison between objective interestingness measures and real human interest in medical data mining

Miho Ohsaki, Yoshinori Sato, Shinya Kitaguchi, Hideto Yokoi, Takahira Yamaguchi

研究成果: Conference article


This research empirically investigates the performance of conventional rule interestingness measures and discusses their availability to supporting KDD through system-human interaction in medical domain. We compared the evaluation results by a medical expert and that by selected measures for the rules discovered from a dataset on hepatitis. Recall and 2 Measure 1 demon-strated the highest performance, and all measures showed different trends under our experimental conditions. These results indicated that some measures can predict really interesting rules at a certain level and that their combinational use in system-human interaction will be useful.

ジャーナルLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
出版物ステータスPublished - 2004 12 9
イベント17th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004 - Ottowa, Ont., Canada
継続期間: 2004 5 172004 5 20


ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)