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Title: Intuitionistic fuzzy density based spatial clustering of applications with noise: IFDBSCAN
Authors: Ünver, Mustafa
Erginel, Nihal
Keywords: Clustering
Intuitionistic fuzzy sets
Unsupervised machine learning
Issue Date: 2020
Publisher: Springer Verlag
Abstract: Density-Based Spatial Clustering of Application with Noise (DBSCAN) is a well-known, unsupervised machine learning tool for clustering. DBSCAN creates clusters based on dense regions while marking points that lie alone in low-density regions as outliers. In DBSCAN, the density is detected via core points which are quite sensitive to input parameters: ? is radius of the neighborhood and MinPts is minimum number of points constraint within ? radius. In contrast to crisp core point definition, intuitionistic fuzzy core point definition makes DBSCAN algorithm capable to detect different patterns of density by two different combinations of input parameters. In this study, a DBSCAN extension is proposed based on this idea: IFDBSCAN. The proposed extension is then illustrated by a computational experiment on a synthetic dataset. Results show that, IFDBSCAN can establish fine-tuned clusters relatively to classical DBSCAN and provide users more insight on the estimation of input parameters. © 2020, Springer Nature Switzerland AG.
Description: International Conference on Intelligent and Fuzzy Systems, INFUS 2019 -- 23 July 2019 through 25 July 2019 -- -- 228529
ISBN: 9.78E+12
ISSN: 2194-5357
Appears in Collections:Makine Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu

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