CutESC: Cutting edge spatial clustering technique based on proximity graphs. (December 2019) - Aksac, Alper; Özyer, Tansel; Alhajj, Reda
Highlights: We propose a cut-edge algorithm for spatial clustering (CutESC) based on proximity graphs. The CutESC algorithm removes edges when a dynamically calculated cut-edge value for the edge's endpoints is below a threshold. The dynamic cut-edge value is calculated by using statistical features and spatial distribution of data based on its neighborhood. The algorithm works without any prior information and preliminary parameter settings while automatically discovering clusters with non-uniform densities, arbitrary shapes, and outliers. There is also an option which allows users to set two parameters to better adapt clustering solutions for particular problems. Experiments have been conducted on various two-dimensional synthetic data and image segmentation to assess advantages of the CutESC algorithm. Abstract: In this paper, we propose a cut-edge algorithm for spatial clustering (CutESC) based on proximity graphs. The CutESC algorithm removes edges when a cut-edge value for the edge's endpoints is below a threshold. The cut-edge value is calculated by using statistical features and spatial distribution of data based on its neighborhood. Also, the algorithm works without any prior information and preliminary parameter settings while automatically discovering clusters with non-uniform densities, arbitrary shapes, and outliers. However, there is an option which allows users to set two parameters to better adapt clustering solutions for particular problems. To assess advantages of CutESC algorithm, experiments have been conducted using various two-dimensional synthetic, high-dimensional real-world, and image segmentation datasets.