Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Interval structure: a framework for representing uncertain information
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Fab: content-based, collaborative recommendation
Communications of the ACM
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Granular Computing on Binary Relations
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Frequent Itemset Counting Across Multiple Tables
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Reduction and axiomization of covering generalized rough sets
Information Sciences: an International Journal
Generalized rough sets based on reflexive and transitive relations
Information Sciences: an International Journal
Mining interesting sets and rules in relational databases
Proceedings of the 2010 ACM Symposium on Applied Computing
Test-cost-sensitive attribute reduction
Information Sciences: an International Journal
Tolerance Approximation Spaces
Fundamenta Informaticae
Granular association rules with four subtypes
GRC '12 Proceedings of the 2012 IEEE International Conference on Granular Computing (GrC-2012)
| Hi-index | 0.00 |
Granular association rules reveal patterns hide in many-to-many relationships which are common in databases. An example of such rules might be "40% men like at least 30% kinds of alcohol; 45% customers are men and 6% products are alcohol." Mining all rules satisfying four thresholds is a challenging problem due to pattern explosion. In this paper, we propose a new type of parametric rough sets on two universes to study this problem. The model is deliberately defined such that the parameter corresponds to one threshold of rules. With the lower approximation operator in the new parametric rough sets, a backward algorithm is designed to deal with the rule mining problem. Experiments on a real world dataset show that the new algorithm is significantly faster than the existing sandwich algorithm. This study builds connections among granular computing, association rule mining and rough sets.