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...
Inspired by our past manual aspect mining experiences, this paper describes a random walk model to approximate how crosscutting concerns can be discovered in the absence of domain...
The mining of frequent patterns in databases has been studied for several years. However, the real-world data tends to be dirty and frequent pattern mining which extracts patterns...
We developed a model based on nonparametric Bayesian modeling for automatic discovery of semantic relationships between words taken from a corpus. It is aimed at discovering seman...
The preference model introduced in this paper gives a natural framework and a principled solution for a broad class of supervised learning problems with structured predictions, su...