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Automatic Data Clustering Based Mean Best Artificial Bee Colony Algorithm

Ayat Alrosan1, Waleed Alomoush2, Mohammed Alswaitti3,*, Khalid Alissa4, Shahnorbanun Sahran5, Sharif Naser Makhadmeh6, Kamal Alieyan7

1 Deanship of Information and Communication Technology, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
2 Computer Department, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
3 School of Electrical and Computer Engineering, Department of Information and Communication Technology, Xiamen University Malaysia, Sepang, 43900, Malaysia
4 Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
5 Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia
6 School of Computer Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia
7 Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan

* Corresponding Author: Mohammed Alswaitti. Email:

Computers, Materials & Continua 2021, 68(2), 1575-1593.


Fuzzy C-means (FCM) is a clustering method that falls under unsupervised machine learning. The main issues plaguing this clustering algorithm are the number of the unknown clusters within a particular dataset and initialization sensitivity of cluster centres. Artificial Bee Colony (ABC) is a type of swarm algorithm that strives to improve the members’ solution quality as an iterative process with the utilization of particular kinds of randomness. However, ABC has some weaknesses, such as balancing exploration and exploitation. To improve the exploration process within the ABC algorithm, the mean artificial bee colony (MeanABC) by its modified search equation that depends on solutions of mean previous and global best is used. Furthermore, to solve the main issues of FCM, Automatic clustering algorithm was proposed based on the mean artificial bee colony called (AC-MeanABC). It uses the MeanABC capability of balancing between exploration and exploitation and its capacity to explore the positive and negative directions in search space to find the best value of clusters number and centroids value. A few benchmark datasets and a set of natural images were used to evaluate the effectiveness of AC-MeanABC. The experimental findings are encouraging and indicate considerable improvements compared to other state-of-the-art approaches in the same domain.


Cite This Article

A. Alrosan, W. Alomoush, M. Alswaitti, K. Alissa, S. Sahran et al., "Automatic data clustering based mean best artificial bee colony algorithm," Computers, Materials & Continua, vol. 68, no.2, pp. 1575–1593, 2021.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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