Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2650
Title: CutESC: Cutting edge spatial clustering technique based on proximity graphs
Authors: Aksaç, Alper
Özyer, Tansel
Alhajj, Reda
143116
Keywords: Spatial data mining
clustering
proximity graphs
graph theory
Issue Date: Dec-2019
Publisher:  Elsevier Ltd
Source: Aksac, A., Özyer, T., and Alhajj, R. (2019). CutESC: Cutting Edge Spatial Clustering Technique based on Proximity Graphs. Pattern Recognition.
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. Published by Elsevier Ltd.
URI: https://www.sciencedirect.com/science/article/pii/S0031320319302468?via%3Dihub
https://hdl.handle.net/20.500.11851/2650
ISSN: 0031-3203
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Yapay Zeka Mühendisliği Bölümü / Department of Artificial Intelligence Engineering

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