Mining graph patterns in large networks is critical to a variety of applications such as malware detection and biological module discovery. However, frequent subgraphs are often i...
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...
Recently, the data stream, which is an unbounded sequence of data elements generated at a rapid rate, provides a dynamic environment for collecting data sources. It is likely that ...
We present a generalization of frequent itemsets allowing the notion of errors in the itemset definition. We motivate the problem and present an efficient algorithm that identifie...
We propose a method for induction of compact optimal recommendation policies based on discovery of frequent itemsets in a purchase database, followed by the application of standar...