An improved unsupervised feature selection using fuzzy C-means clustering and Archimedes optimization algorithm
DOI:
https://doi.org/10.62933/hcfkd736Keywords:
fuzzy c-mean algorithm, Clustering , Archimedes Optimization Algorithm (AOA), . Binary Archimedes optimization algorithmAbstract
In this research, we propose a new choice of plant selection based on Archimedes Optimization Sales (BAS) in combination with a modified fuzzy c-mean algorithm. With marked patterns, BAS increases the unsecured learning of traditional unclear C-mines. Using these annotate patterns, BAS is able to model the geometry of each cluster correctly, emphasizing the properties that are most useful for discrimination against clusters. Patterns with high levels of cluster membership show small variance in the values of these functions. These symptoms have strong discriminatory properties between clusters and other groups if there is a significant distance between the prototype of a cluster and the prototype of other clusters. We used BAS to classify patterns on many real benchmarks. Our learning method was the popular K-nest neighbor (FCM). Our results showed that the functional elections made by BAS increased generalization in all data sets.
References
References
1. Abdollahi, M., et al., Imperialist competitive algorithm for solving systems of nonlinear equations. 2013. 65(12): p. 1894-1908.
2. K, S., Feature Selection using K-Means Clustering for Data Mining. Conference: International Symposia in Advance Computing (ISAC'2K11), 2011.
3. Sutar, S., Feature selection algorithm using fast clustering and correlation measure. 2015. 2(7): p. 236-241.
4. Mahmood, S.G., R.S. Karyakos, and I.M.J.E.-E.J.o.E.T. Yacoob, Hybrid gene selection method based on mutual information technique and dragonfly optimization algorithm. 2021. 3(3): p. 111.
5. Cai, D., C. Zhang, and X. He. Unsupervised feature selection for multi-cluster data. in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. 2010.
6. Zawbaa, H.M., et al., Large-dimensionality small-instance set feature selection: A hybrid bio-inspired heuristic approach. 2018. 42: p. 29-42.
7. Li, J., et al., Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning. 2024. 11(1): p. 36.
8. Cheng, X.J.I.i.C., Signals and Systems, A Comprehensive Study of Feature Selection Techniques in Machine Learning Models. 2024. 1(1): p. 10.70088.
9. Al-kababchee, S.G.M., Z.Y. Algamal, and O.S.J.J.o.I.S. Qasim, Enhancement of K-means clustering in big data based on equilibrium optimizer algorithm. 2023. 32(1): p. 20220230.
10. Krishnasamy, G., A.J. Kulkarni, and R. Paramesran, A hybrid approach for data clustering based on modified cohort intelligence and K-means. Expert Systems with Applications, 2014. 41(13): p. 6009-6016.
11. MacQueen, J. Some methods for classification and analysis of multivariate observations. in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. 1967. Oakland, CA, USA.
12. Arora, J., K. Khatter, and M.J.S.E.P.o.C. Tushir, Fuzzy c-means clustering strategies: A review of distance measures. 2019: p. 153-162.
13. Pacifico, L.D.S. and T.B. Ludermir, An evaluation of k-means as a local search operator in hybrid memetic group search optimization for data clustering. Natural Computing, 2020.
14. Krasnov, D., et al., Fuzzy c-means clustering: A review of applications in breast cancer detection. 2023. 25(7): p. 1021.
15. Xu, J., et al. Robust and sparse fuzzy k-means clustering. in IJCAI. 2016.
16. Suganya, R., R.J.I.J.o.S. Shanthi, and R. Publications, Fuzzy c-means algorithm-a review. 2012. 2(11): p. 1.
17. Nurmuhammed, M., et al., Modified Archimedes optimization algorithm for global optimization problems: A comparative study. 2024. 36(14): p. 8007-8038.
18. Ge, Q., et al., Support Vector Machine to Predict the Pile Settlement using Novel Optimization Algorithm. 2023: p. 1-15.
19. Allogmani, A.S., et al., Enhanced cervical precancerous lesions detection and classification using Archimedes Optimization Algorithm with transfer learning. 2024. 14(1): p. 12076.
20. Hashim, F.A., et al., Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. 2021. 51: p. 1531-1551.
21. Fang, L., Y. Yao, and X.J.E.S.w.A. Liang, New binary archimedes optimization algorithm and its application. 2023. 230: p. 120639.
22. Mohamed, B., et al., Binary Archimedes Optimization Algorithm for Computing Dominant Metric Dimension Problem. 2023. 38(1).
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Sarah Ghanim Mahmood Al- Kababchee, Ilham M Yacoob, Omar Saber Qasim3, Zakariya Yahya Algamal (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Licensed under a CC-BY license: https://creativecommons.org/licenses/by-nc-sa/4.0/
