This exploratory data mining project used distance-based clustering algorithms to study three indicators of student behavioral data collectively called AB-Index, and established a typology of six types of learners for a suburban community college. The study is based on the notion that student behavioral data are a good basis for new ways of doing research studies rather than using non-behavioral data, such as gender or race and intended educational goals. The discoveries from this data mining endeavor are meaningful for understanding and measuring students' behaviors. The study encapsulated and discussed several fresh and novel topics and analytical approaches. The study uncovered previously unknown differences in both output (FTES) and outcomes (GPA, Persistence) across the learner types which may greatly enhance a college's ability to monitor the changes and to make appropriate adjustment to enrollment and teaching strategies. The study noted the lack of predictive power of traditional indicators, such as race or gender, across learner types within the typology. The study also employed several less often used data visualization techniques, such as drop-line charts and the Web graph. (Contains 5 endnotes, 10 figures, 12 tables, and a bibliography.)