Dynamic miss-counting algorithms: finding implication and similarity rules with confidence pruning

Abstract

Dynamic miss-counting (DMC) algorithms are proposed which find all implication and similarity rules with confidence pruning but without support pruning. To handle data sets with a large number of columns, we propose dynamic pruning techniques that can be applied during data scanning. DMC counts the numbers of rows in which each pair of columns disagree instead of counting the number of hits. DMC deletes a candidate as soon as the number of misses exceeds the maximum number of misses allowed for that pair. We also propose several optimization techniques that reduce the required memory size significantly. We evaluated our algorithms by using four data sets, viz. Web access logs, a Web page-link graph, news documents and a dictionary. These data sets have between 74,000 and 700,000 items. Experiments show that DMC can find high-confidence rules for such large data sets efficiently.