Finding input sub-spaces for polymorphic fuzzy signatures

A significant feature of fuzzy signatures is its applicability for complex and sparse data. To create Polymorphic Fuzzy Signatures (PFS) for sparse data, sparse input sub-spaces (ISSs) should be considered. Finding the optimal ISSs manually is not a simple task as it is time consuming; moreover, some knowledge about the dataset is necessary. Fuzzy C-Means (FCM) clustering employed with a trapezoidal approximation method is needed to find ISSs automatically. Furthermore, dealing with sparse data, we should be mindful about choosing a reliable trapezoidal approximation method. This facilitates the optimal ISS creation for the data. In our experiment, two trapezoidal approximation methods were used to find optimal ISSs. The results demonstrate that our version of trapezoidal approximation for creating ISSs result in an PFS with lower mean square error compared to the original trapezoidal approximation method.