Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)

<Previous Article In Volume
Next Article In Volume>

A Novel Improved Fractional Rough Fuzzy K-Means (IFRFKM) Algorithm to Solve Data Clustering Problem

Authors
K. Srikanth1, 2, *, S. Zahoor Ul Huq3, A. P. Siva Kumar4
1Research Scholar, Department of CSE, JNTUA, Ananthapuramu, India
2Associate Professor, Department of CSE, G. Pulla Reddy Engineering College, Kurnool, India
3Professor, Department of CSE, G. Pulla Reddy Engineering College, Kurnool, India
4Professor, Department of CSE, JNTUA, Ananthapuramu, India
*Corresponding author. Email: kapse.srikanth@gmail.com
Corresponding Author
K. Srikanth
Available Online 17 March 2025.
DOI
10.2991/978-94-6463-662-8_61How to use a DOI?
Keywords
Clustering; IF-RFKM; K-Means
Abstract

Data instances are organized into clusters through a machine learning technique known as clustering. This method is employed to categorize each data instance within a collection into specific groups. Ideally, data instances in different groups exhibit distinct attributes, while those within the same group share similar characteristics. Clustering, an essential tool in statistical data analysis, falls under the category of unsupervised learning. By leveraging clustering techniques, we can gain valuable insights from our data by identifying group memberships among the data instances. The primary objective of grouping is to categorize data instances based on their similarities and differences. To attain this, an appropriate grouping algorithm divides the dataset into multiple groups, minimizing the similarities within each group. In this context, we recommend using the IFRFKM algorithm for classifying instances based on their membership function similarities. Results from five well-established datasets—the diagnostic WDBC, original WBCD, Glass, Thyroid, and Wine—demonstrate that the IF-RFKM method significantly surpasses the effectiveness of existing grouping algorithms.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
Series
Advances in Engineering Research
Publication Date
17 March 2025
ISBN
978-94-6463-662-8
ISSN
2352-5401
DOI
10.2991/978-94-6463-662-8_61How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - K. Srikanth
AU  - S. Zahoor Ul Huq
AU  - A. P. Siva Kumar
PY  - 2025
DA  - 2025/03/17
TI  - A Novel Improved Fractional Rough Fuzzy K-Means (IFRFKM) Algorithm to Solve Data Clustering Problem
BT  - Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
PB  - Atlantis Press
SP  - 772
EP  - 783
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-662-8_61
DO  - 10.2991/978-94-6463-662-8_61
ID  - Srikanth2025
ER  -