A Design of Granular Model Using Conditional Clustering and Density Peaks

In this paper, an improved conditional clustering method based on rapid search of density peaks to construct granular model is proposed. This clustering generates linguistic contexts in the output space and estimates the cluster centers so that possess the homogeneity between input and output space. Furthermore, it can obtain the valid number of cluster corresponding to each context by rapidly searching density peaks using the correlation between local density and distance index from high density points. Thus, this proposed clustering approach can be used as if-then rules with unique characteristics of granular model. It also has the prediction performance by the model output with uncertainty. For this, the experiments are performed on simple synthetical data sets and real-world application problem to demonstrate the superiority and effectiveness through the efficient rule extraction and design of granular model.