The Data Abstraction Research Group was formed in the early 1990s, to bring focus to the Mathematical Sciences department’s work in the emerging area of Knowledge Discovery and Data Mining (KD&DM). Most activities in this group have been performed in the technical area of predictive modeling, roughly at the intersection of machine learning, statistical modeling, and database technology. There has been a major emphasis on using business and industrial problems to drive the research agenda. Major accomplishments include advances in methods for feature analysis, rule based pattern discovery and probabilistic modeling, and novel solutions for insurance risk management, targeted marketing, and text mining. This paper presents an overview of the group’s major technical accomplishments. 1