In this paper, we propose a set of novel regression-based approaches to effectively and efficiently summarize frequent itemset patterns. Specifically, we show that the problem of ...
Transactional data are ubiquitous. Several methods, including frequent itemsets mining and co-clustering, have been proposed to analyze transactional databases. In this work, we p...
Yang Xiang, Ruoming Jin, David Fuhry, Feodor F. Dr...
We propose a visualization method based on a topic model for discrete data such as documents. Unlike conventional visualization methods based on pairwise distances such as multi-d...
Modeling the evolution of topics with time is of great value in automatic summarization and analysis of large document collections. In this work, we propose a new probabilistic gr...
Ramesh Nallapati, Susan Ditmore, John D. Lafferty,...
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...