A Measure for Improvement in Quality of Association Rules in the Item Response Dataset

문항 응답 데이터에서 문항간 연관규칙의 질적 향상을 위한 도구 개발

  • 곽은영 (고려대학교 컴퓨터교육과) ;
  • 김현철 (고려대학교 컴퓨터교육과)
  • Received : 2006.10.23
  • Accepted : 2007.01.25
  • Published : 2007.05.31

Abstract

In this paper, we introduce a new measure called surprisal that estimates the informativeness of transactional instances and attributes in the item response dataset and improve the quality of association rules. In order to this, we set artificial dataset and eliminate noisy and uninformative data using the surprisal first, and then generate association rules between items. And we compare the association rules from the dataset after surprisal-based pruning with support-based pruning and original dataset unpruned. Experimental result that the surprisal-based pruning improves quality of association rules in question item response datasets significantly.