Data mining provides both systematic and systemic ways to detect patterns of student engagement among students at hundreds of institutions. Using traditional statistical techniques alone, the task would be significantly difficult--if not impossible--considering the size and complexity in both data and analytical approaches necessary for this task. This study presents a step-by-step review on how the data mining technique is utilized to develop an institutional typology based on student behavioral data. The result provides a fresh angle to understand similarities and differences among four-year undergraduate colleges and universities, shifting away from previous institutional typologies, such as those based on institutional mission, resources, or reputation. The institutional engagement typology is derived through student behavioral data, and therefore, is advantageous in that it retains one of the most important components in understanding higher education--student behaviors. This data mining-based study broke new conceptual and methodological ground, and its resulting institutional learning engagement typology offers new perspectives on peer institution comparison, congruence between students and their institutions, as well as policy development regarding educational quality. (Contains 8 footnotes, 3 figures and 6 tables.)