Designing a Classifier with KFCM Algorithm to Achieve Optimization of Clustering and Classification Simultaneously

Provided by: International Journal of Emerging Technology and Advanced Engineering (IJETAE) Topic: Data Management Format: PDF
In this paper, design a classifier with KFCM algorithm to achieve Optimization of Clustering and Classification simultaneously (KOCC) is proposed. This learning algorithm is used to solve any multiclass classification problem. In this Kernalized Fuzzy C-Means (KFCM) algorithm is used for enhance the robustness of the classifier. The training phase of the KOCC classifier proceeds in two phases: first phase of the KOCC classifier is the clustering phase as the clustering phase, the dataset is clustered in an unsupervised manner (i.e. class labels are not used during clustering process) using the modification proposed in Kernalized Fuzzy C-Means clustering (KFCM) algorithm.

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