Algorithms for clustering data
Algorithms for clustering data
ACM Computing Surveys (CSUR)
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Cluster validity methods: part I
ACM SIGMOD Record
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
Generalised Weighted Relevance Aggregation Operators for Hierarchical Fuzzy Signatures
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Learning Generalized Weighted Relevance Aggregation Operators Using Levenberg-Marquardt Method
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Improvements and critique on Sugeno's and Yasukawa's qualitative modeling
IEEE Transactions on Fuzzy Systems
A generalized concept for fuzzy rule interpolation
IEEE Transactions on Fuzzy Systems
Fuzzy rule interpolation for multidimensional input spaces with applications: a case study
IEEE Transactions on Fuzzy Systems
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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.