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An Enhanced Spectral Clustering Algorithm with S-Distance

Department of Computer Science & Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482005, India
Department of Computer Science & Informatics, University of Kota, Kota, Rajasthan 324022, India
Center for Basic and Applied Science, Faculty of Informatics and Management, University of Hradec Králové, Hradec 50003 Králové, Czech Republic
Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain
Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, Kuala Lumpur 54100, Malaysia
Author to whom correspondence should be addressed.
Academic Editors: Kóczy T. László and István A. Harmati
Symmetry 2021, 13(4), 596;
Received: 4 March 2021 / Revised: 19 March 2021 / Accepted: 25 March 2021 / Published: 2 April 2021
(This article belongs to the Special Issue Computational Intelligence and Soft Computing: Recent Applications)
Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape. View Full-Text
Keywords: S-divergence; S-distance; spectral clustering S-divergence; S-distance; spectral clustering
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MDPI and ACS Style

Kumar Sharma, K.; Seal, A.; Herrera-Viedma, E.; Krejcar, O. An Enhanced Spectral Clustering Algorithm with S-Distance. Symmetry 2021, 13, 596.

AMA Style

Kumar Sharma K, Seal A, Herrera-Viedma E, Krejcar O. An Enhanced Spectral Clustering Algorithm with S-Distance. Symmetry. 2021; 13(4):596.

Chicago/Turabian Style

Kumar Sharma, Krishna, Ayan Seal, Enrique Herrera-Viedma, and Ondrej Krejcar. 2021. "An Enhanced Spectral Clustering Algorithm with S-Distance" Symmetry 13, no. 4: 596.

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