This explorative data mining project used distance based clustering algorithm to study 3 indicators, called OIndex, of student behavioral data and stabilized at a 6-cluster scenario following an exhaustive explorative study of 4, 5, and 6 cluster scenarios produced by K-Means and TwoStep algorithms. Using principles in data mining, the study followed a proven data mining process that proceeded from identifying the research questions, to staging the data, to data auditing, and to building scenarios. All scenarios were subjected to data visualization, and in cases appropriate, Chi-square analysis. This study established 6 typologies of students enrolled at a suburban community college. The study is based on the notion that student behavioral data are good candidates for new facets of research studies, compared to using non-behavioral data, such as gender or race. The discoveries from this study emerged as both meaningful for understanding and measuring students' learning as well as actionable for decision making. The typologies may be added to existing educational strategies for both management and assessment of learning. (Contains 10 tables and 9 figures.)