Imbalanced Data Clustering Using Improved AFCM Algorithm Based on Whale Optimization Algorithm

Authors

  • Seyedeh Fatemeh Ghaderi Department of Computer Engineering, Da.C., Islamic Azad University, Damghan, Iran.
  • Hamid Abbasi * Department of Computer Engineering, Da.C., Islamic Azad University, Damghan, Iran. https://orcid.org/0000-0001-6471-430X
  • Mojtaba Shokouhinia Department of Computer Engineering, Da.C., Islamic Azad University, Damghan, Iran.

https://doi.org/10.22105/raise.v3i2.89

Abstract

Accurate clustering in data mining faces serious challenges, especially when the data is unbalanced. In unbalanced data, traditional Fuzzy Clustering Algorithms (FCM) usually do not have sufficient accuracy and do not correctly identify minority classes due to their tendency to create clusters of equal size. To reduce the effect of this limitation, in this research, a novel approach is presented in which the adaptive FCM is improved using the Whale Optimization Algorithm (WOA). By purposefully exploring the response space, the whale algorithm adjusts the parameters of the adaptive FCM optimally and therefore reduces the probability of the algorithm getting stuck in local optima. Experimental evaluations on standard Iris and Thyroid datasets show that the proposed method achieves values ​of 97.14% and 94.71% for F1-Score and Balanced Accuracy (BA) metrics, respectively. This improvement in evaluation criteria indicates that the proposed method has a higher ability to manage the side effects of data imbalance and maintain the true structure of clusters compared to FCM and other basic methods.

Keywords:

Clustering, Unbalanced data, Data mining, Adaptive fuzzy algorithm, Whale optimization algorithm

References

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Published

2026-05-27

Issue

Vol. 3 No. 2 (2026): Research Annals of Industrial and Systems Engineering

Section

Articles

How to Cite

Ghaderi, S. F. ., Abbasi, H. ., & Shokouhinia, M. . (2026). Imbalanced Data Clustering Using Improved AFCM Algorithm Based on Whale Optimization Algorithm. Research Annals of Industrial and Systems Engineering, 3(2), 86-97. https://doi.org/10.22105/raise.v3i2.89

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