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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making

ISBN

978-3-031-16202-2

Počet stran výsledku

27

Strana od-do

564-590

Počet stran knihy

721

Název nakladatele

Springer Cham

Místo vydání

Switzerland

Kód UT WoS kapitoly

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