SNP-based information is used in several existing clustering methods to detect shared genetic ancestry or to identify population substructure. Here, we present a methodology, called IPCAPS for unsupervised population analysis using iterative pruning. Our method, which can capture fine-level structure in populations, supports ordinal data, and thus can readily be applied to SNP data. Although haplotypes may be more informative than SNPs, especially in fine-level substructure detection contexts, the haplotype inference process often remains too computationally intensive. In this work, we investigate the scale of the structure we can detect in populations without knowledge about haplotypes; our simulated data do not assume the availability of haplotype information while comparing our method to existing tools for detecting fine-level population substructures. We demonstrate experimentally that IPCAPS can achieve high accuracy and can outperform existing tools in several simulated scenarios. The fine-level structure detected by IPCAPS on an application to the 1000 Genomes Project data underlines its subject heterogeneity.