Authors : Firas Miften and Israa Hadi
Abstract: In recent years, there has been an increasing interest in emerging effective techniques for image clustering. Graph clustering algorithm partitions a set of vertices in graphs into smaller sets (clusters) such that vertices in the same set are related to each other than to those in other sets. This study focuses the problem whether or not the fractal is helpful for graph clustering images. The most of the study used fractal dimension to calculate the self-similarity. In this study, we present a new algorithm, based on matching rang and domain fractal to find self-similarity properties of the data sets which can be used for graph clustering. It suggests that using fractal for image graph clustering can get the effective results. And this study presents an algorithm that automatically finds the number of clusters based on shared neighbors among vertices. The proposed algorithm is able to efficiently find graph clustering partitions for whole graphs.
Firas Miften and Israa Hadi, 2018. Graph Clustering for Images Based on Fractal Features. Journal of Engineering and Applied Sciences, 13: 2274-2281.