An enhanced spectral clustering algorithm with s-distance

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50018011" target="_blank" >RIV/62690094:18450/21:50018011 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2073-8994/13/4/596" target="_blank" >https://www.mdpi.com/2073-8994/13/4/596</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/sym13040596" target="_blank" >10.3390/sym13040596</a>

Result language

angličtina

Original language name

An enhanced spectral clustering algorithm with s-distance

Original language description

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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10700 - Other natural sciences

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Symmetry-Basel

ISSN

2073-8994

e-ISSN

—

Volume of the periodical

13

Issue of the periodical within the volume

4

Country of publishing house

CH - SWITZERLAND

Number of pages

17

Pages from-to

"Article number 596"

UT code for WoS article

000643603700001

EID of the result in the Scopus database

2-s2.0-85104180766