Towards an Interpretation of the Connectivity Analysis in the Frame of Fuzzy Equivalence Relations for Medical Diagnoses

Connectivity analysis methodology is suitable to find representative symptoms of a disease. This methodology describes connections between symptoms in particular way and then chooses the group of symptoms that have the high level of connection, or in other words, have strong interconnections between elements of a group. In this paper we investigate the analogy between connectivity analysis and cluster analysis based on fuzzy equivalence relations. A comparison of two approaches, one of which has strong theoretical background (cluster analysis based on fuzzy equivalence relations) and more practically oriented connectivity analysis assures more convincing and accurate connectivity analysis from one side and applicability of fuzzy equivalence relations for medical diagnoses from another. Connectivity analysis, as shown in the paper, is one of the clustering methods, can be used in many applications where feature selection and extraction problem is considered, in particular, in pattern recognition and image processing. The results of the comparison are demonstrated on the examples. Keywords— connectivity, equivalence relations, fuzzy cluster