Association rule mining (ARM) identifies frequent itemsets from databases and generates association rules by assuming that all items have the same significance and frequency of oc...
Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each si...
We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to han...
Protein-protein interactions are of great interest to biologists. A variety of high-throughput techniques have been devised, each of which leads to a separate definition of an int...
Christopher Besemann, Anne Denton, Ajay Yekkirala,...
We explore in this paper a progressive sampling algorithm, called Sampling Error Estimation (SEE), which aims to identify an appropriate sample size for mining association rules. S...