Information-Based pruning for interesting association rule mining in the item response dataset

Authors:
Hyeoncheol Kim;Eun-Young Kwak
Affiliations:
Department of Computer Science Education, College of Education, Korea University, Seoul, Korea;Department of Computer Science Education, College of Education, Korea University, Seoul, Korea
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2005

Citing 4
Cited 1

Data mining: concepts and techniques

Data mining: concepts and techniques
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules

Data Mining and Knowledge Discovery
Exception Rule Mining with a Relative Interestingness Measure

PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Selecting the right interestingness measure for association patterns

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining

New prediction model for pre-fetching in mobile database

Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services

Quantified Score

Hi-index	0.00

Visualization

Abstract

Frequency-based mining of association rules sometimes suffers rule quality problems. In this paper, we introduce a new measure called surprisal that estimates the informativeness of transactional instances and attributes. We eliminate noisy and uninformative data using the surprisal first, and then generate association rules of good quality. Experimental results show that the surprisal-based pruning improves quality of association rules in question item response datasets significantly.