Social data refers to data individuals create that is knowingly and voluntarily shared by them and is an exciting avenue into gaining insight into interpersonal behaviors and interaction. However, such data is large, heterogeneous and often incomplete, properties that make the analysis of such data extremely challenging. One common method of exploring such data is through cluster analysis, which can enable analysts to find groups of related users, behaviors and interactions. This paper presents a novel visual analysis approach for detecting clusters within large-scale social networks by utilizing a divide-analyze-recombine scheme that sequentially performs data partitioning, subset clustering and result recombination within an integrated visual interface. A case study on a microblog messaging data (with 4.8 millions users) is used to demonstrate the feasibility of this approach and comparisons are also provided to illustrate the performance benefits of this approach with respect to existing solutions.