More specifically, a new weighted heterogeneous similarity function is also proposed to estimate relationships among interactive events. In the second phase of the framework, we combine the pattern-recognition techniques with network-based approaches. In this research, we propose a new hybrid detection framework in the proposed network topology.
As a result, the networks (as seen in Figure 2) are built to see how the events are similar and how they interact with each other. Based on the network metrics such as degree centrality, closeness centrality, betweenness centrality, in-degree centrality, out-degree centrality, load centrality and harmonic centrality, the pattern recognition techniques are applied to detect the credit card approval, breast cancer diagnosing, schizophrenia disease in fMRI, and diabetic disease. In conclusion, the proposed approach was tested and validated using real world case studies.