Knowledge discovery in databases has become an increasingly important research topic with the advent of wide area network computing. One of the crucial problems we study in this p...
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
Complexity of post-genomic data and multiplicity of mining strategies are two limits to Knowledge Discovery in Databases (KDD) in life sciences. Because they provide a semantic fr...
In this paper, we propose a novel approach for automatic generation of visualizations from domain-specific data available on the web. We describe a general system pipeline that co...
It has been shown in prior work in management science, statistics and machine learning that using an ensemble of models often results in better performance than using a single ‘...