Next Article in Journal
Threat Defense: Cyber Deception Approach and Education for Resilience in Hybrid Threats Model
Next Article in Special Issue
Extractive Summarization Based on Dynamic Memory Network
Previous Article in Journal
Periodic Solutions of Nonlinear Relative Motion Satellites
Previous Article in Special Issue
On Present Value Evaluation under the Impact of Behavioural Factors Using Oriented Fuzzy Numbers
 
 
Article

An Enhanced Spectral Clustering Algorithm with S-Distance

1
Department of Computer Science & Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur 482005, India
2
Department of Computer Science & Informatics, University of Kota, Kota, Rajasthan 324022, India
3
Center for Basic and Applied Science, Faculty of Informatics and Management, University of Hradec Králové, Hradec 50003 Králové, Czech Republic
4
Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain
5
Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
6
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; https://doi.org/10.3390/sym13040596
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
Show Figures

Figure 1

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. https://doi.org/10.3390/sym13040596

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. https://doi.org/10.3390/sym13040596

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. https://doi.org/10.3390/sym13040596

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop