Recent advances in high throughput data collection and imaging technologies have resulted in the availability of diverse biomedical datasets that capture complementary information pertaining to the biological processes in an organism. Biomarkers that are discovered by integrating such datasets obtained from case-control studies have the potential to elucidate the biological mechanisms behind complex human diseases. Of particular importance are interaction-type integrative biomarker, which are biomarkers whose features can explain the disease when taken together, but not when considered individually. We propose a pattern mining based integrative framework (PAMIN) to discover these interactiontype integrative biomarkers from diverse case control datasets. PAMIN first finds patterns from individual datasets to capture the available information separately and then combines these patterns to find integrated patterns (IPs) consisting of variables from multiple datasets. We also use several interestingness measures to characterize the IPs into specific categories. Using synthetic and real data we compare the IPs found using our approach with those found by CCA and discriminative-CCA (dCCA). Our results indicate that PAMIN is able to discover interaction type integrated patterns that these competing approaches cannot find.