Three-Way Clustering Based on Digital Image Processing

A three-way clustering algorithm based on image processing is proposed by combining blurring and sharpening operations in digital image processing. The proposed algorithm quantifies the density of the dataset into gray values through inverse multivariate quadratic kernel function. By blurring and sharpening operations for the university, we delete the samples with low density and obtain samples with high density. Then, different clusters are produced by traditional clustering algorithm for samples with high density. For each cluster, the core region and fringe region are obtained through blurring operation and sharpening operation, respectively. The experiments on different UCI datasets verify that the clustering algorithm is stable and effective by comparing the clustering indexes adjusted rand index (ARI), normalized mutual information (NMI) and adjusted mutual information (AMI).