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...
In this paper, we propose the "Democratic Classifier", a simple, democracy-inspired patternbased classification algorithm that uses very short patterns for classificatio...
Decision support systems are important in leveraging information present in data warehouses in businesses like banking, insurance, retail and health-care among many others. The mu...
Mining frequent itemsets in data streams is beneficial to many real-world applications but is also a challenging task since data streams are unbounded and have high arrival rates...
The focus of this paper is on how to select a small sample of examples for labeling that can help us to evaluate many different classification models unknown at the time of sampl...