In this work we focus on the problem of frequent itemset mining on large, out-of-core data sets. After presenting a characterization of existing out-of-core frequent itemset minin...
Mining frequent patterns in a data stream is very challenging for the high complexity of managing patterns with bounded memory against the unbounded data. While many approaches as...
Multi-core processors are proliferated across different domains in recent years. In this paper, we study the performance of frequent pattern mining on a modern multi-core machine....
Traditional association mining algorithms use a strict definition of support that requires every item in a frequent itemset to occur in each supporting transaction. In real-life d...
Rohit Gupta, Gang Fang, Blayne Field, Michael Stei...
In this paper, we propose a novel probabilistic approach to summarize frequent itemset patterns. Such techniques are useful for summarization, post-processing, and end-user interp...