Integrating Decision Theory and Syntactic Data for Enhanced Rough Fuzzy C-Means Clustering Algorithm-Reference-Cited by-同舟云学术

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Integrating Decision Theory and Syntactic Data for Enhanced Rough Fuzzy C-Means Clustering Algorithm

Published:2023-12-11 Issue: Volume:41 Page:781-785
ISSN:
Container-title:2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS)
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Author:

Ammisetty Veeraswamy¹,Ande Vs Sudhakar Rao²,Babu D Kishore³,Baburao Mikkili⁴

Affiliation:

1. Koneru Lakshmaiah Education Foundation, Green Fileds,Department of Computer Science & Engineering,Vaddeswaram,A.P,522302

2. Vasireddy Venkatadri Institute of Technology, Nambur,Department of Information Technology,Guntur,Andhra Pradesh,India

3. Bapatla Engineering College,Department of CSE,Bapatla,Andhra Pradesh,India

4. Bapatla Engineering College,Department CS& DS,Bapatla,Andhra Pradesh,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10404046/10404076/10404288.pdf?arnumber=10404288

Reference8 articles.

1. On cluster validity for the fuzzy c-means model

2. Fast Fuzzy C-Means Clustering Algorithms Using Membership Filter and Defuzzification with Application to Image Segmentation;Wu;Pattern Recognition,2008

3. Convergence theory for fuzzy c-means: Counterexamples and repairs

4. A novel rough fuzzy clustering algorithm for pattern recognition;Song;Pattern Recognition,2011

5. MCSC-Net: COVID-19 detection using deep-Q-neural network classification with RFNN-based hybrid whale optimization

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