Fuzzy Clustering has the advantage of offering degrees of compatibility, rather than declaring elements strictly related or unrelated as in classical clustering. Initially, we will work with the Fuzzy c-Means algorithm, where the user determines the number of clusters. The method calculates the designated number of cluster centers, or prototypes, and then assigns a measure of compatibility between each element of the data set and each cluster center. In this way, data points are members of all clusters, to some degree, rather than being assigned to only one. Identifying the genes that are moderately related to a number of prototypes may be as significant as identifying those that are strongly related to only one or two prototypes.
Fuzzy clustering is a means of identifying both groups.
The domain of the problem data is still in the defining stage. We are currently working to clarify the source of data with the functional genomics project and how to represent the specific data appropriately in the frameworks mentioned above. L. Pickert, I. Reuter, F. Klawonn and E.Wingender .