A Noise Resistant Credibilistic Fuzzy Clustering Algorithm on a Unit Hypersphere with Illustrations Using Expression Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F23%3AA2402G4V" target="_blank" >RIV/61988987:17610/23:A2402G4V - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-16203-9_32" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-16203-9_32</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-16203-9_32" target="_blank" >10.1007/978-3-031-16203-9_32</a>

Result language
angličtina
Original language name
A Noise Resistant Credibilistic Fuzzy Clustering Algorithm on a Unit Hypersphere with Illustrations Using Expression Data
Original language description
This article presents a robust noise-resistant fuzzy-based algorithm for cancer class detection. High-throughput microarray technologies facilitate the generation of large-scale expression data; this data captures enough information to build classifiers to understand the molecular basis of a disease. The proposed approach built on the Credibilistic Fuzzy C-Means (CFCM) algorithm partitions data restricted to a p-dimensional unit hypersphere. CFCM was introduced to address the AQ2 noise sensitiveness of fuzzy-based procedures, but it is unstable and fails to capture local non-linear interactions. The introduced approach addresses these shortcomings. The experimental findings in this article focus on cancer expression datasets. The performance of the proposed approach is assessed with both internal and external measures. The fuzzy-based learning algorithms Fuzzy C-Means (FCM) and Hyperspherical Fuzzy C-Means (HFCM) are used for comparative analysis. The experimental findings indicate that the proposed approach can be used as a plausible tool for clustering cancer expression data.
Czech name
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Czech description
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Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name
Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making
ISBN
978-3-031-16202-2
Number of pages of the result
27
Pages from-to
564-590
Number of pages of the book
721
Publisher name
Springer Cham
Place of publication
Switzerland
UT code for WoS chapter
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