Correlated Itemset Mining in ROC Space: A Constraint Programming Approach
published: Sept. 14, 2009, recorded: July 2009, views: 128
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Correlated or discriminative pattern mining is concerned with finding the highest scoring patterns w.r.t. a correlation measure (such as information gain). By reinterpreting correlation measures in ROC space and formulating correlated itemset mining as a constraint programming problem, we obtain new theoretical insights with practical benefits. More specifically, we contribute 1) an improved bound for correlated itemset miners, 2) a novel iterative pruning algorithm to exploit the bound, and 3) an adaptation of this algorithm to mine all itemsets on the convex hull in ROC space. The algorithm does not depend on a minimal frequency threshold and is shown to outperform several alternative approaches by orders of magnitude, both in runtime and in memory requirements.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !