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Premkumar M, Sinha G, Ramasamy MD, Sahu S, Subramanyam CB, Sowmya R, Abualigah L, Derebew B. Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems. Sci Rep 2024; 14:5434. [PMID: 38443569 PMCID: PMC10914809 DOI: 10.1038/s41598-024-55619-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/26/2024] [Indexed: 03/07/2024] Open
Abstract
This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems.
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Affiliation(s)
- Manoharan Premkumar
- Department of Electrical & Electronics Engineering, Dayananda Sagar College of Engineering, Kumaraswamy Layout, Bengaluru, Karnataka, 560078, India.
| | - Garima Sinha
- Department of Computer Science and Engineering, Jain University, Ramanagaram, Bengaluru, Karnataka, India
| | - Manjula Devi Ramasamy
- Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
| | - Santhoshini Sahu
- Department of Computer Science & Engineering, GMR Institute of Technology, Rajam, Srikakulam, Andhra Pradesh, India
| | | | - Ravichandran Sowmya
- Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Laith Abualigah
- Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan
- Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, 71491, Tabuk, Saudi Arabia
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
- MEU Research Unit, Middle East University, Amman, 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
- School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia
- College of Engineering, Yuan Ze University, Taoyuan, Taiwan
- Department of Statistics, College of Natural and Computational Science, Mizan-Tepi University, Tepi Bushira, Ethiopia
| | - Bizuwork Derebew
- Applied science research center, Applied science private university, Amman, 11931, Jordan.
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Singh H, Kumar Y. An Enhanced Version of Cat Swarm Optimization Algorithm for Cluster Analysis. INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING 2022. [DOI: 10.4018/ijamc.2022010108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Clustering is an unsupervised machine learning technique that optimally organizes the data objects in a group of clusters. In present work, a meta-heuristic algorithm based on cat intelligence is adopted for optimizing clustering problems. Further, to make the cat swarm algorithm (CSO) more robust for partitional clustering, some modifications are incorporated in it. These modifications include an improved solution search equation for balancing global and local searches, accelerated velocity equation for addressing diversity, especially in tracing mode. Furthermore, a neighborhood-based search strategy is introduced to handle the local optima and premature convergence problems. The performance of enhanced cat swarm optimization (ECSO) algorithm is tested on eight real-life datasets and compared with the well-known clustering algorithms. The simulation results confirm that the proposed algorithm attains the optimal results than other clustering algorithms.
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Affiliation(s)
| | - Yugal Kumar
- Jaypee University of Infromation Technoogy, India
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Asadi-Zonouz M, Amin-Naseri MR, Ardjmand E. A modified unconscious search algorithm for data clustering. EVOLUTIONARY INTELLIGENCE 2021. [DOI: 10.1007/s12065-021-00578-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Singh H, Kumar Y. Cellular Automata Based Model for E-Healthcare Data Analysis. INTERNATIONAL JOURNAL OF INFORMATION SYSTEM MODELING AND DESIGN 2019. [DOI: 10.4018/ijismd.2019070101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
E-healthcare is warm area of research and a number of algorithms have been applied to classify healthcare data. In the healthcare field, a large amount of clinical data is generated through MRI, CT scans, and other diagnostic tools. Healthcare analytics are used to analyze the clinical data of patient records, disease diagnosis, cost, hospital management, etc. Analytical techniques and data visualization are used to get the real time information. Further, this information can be used for decision making. Also, this information is useful for the better treatment of patients. In this work, an improved big bang-big crunch (BB-BC) based clustering algorithm is applied to analyze healthcare data. Cluster analysis is an important task in the field of data analysis and can be used to understand the organization of data. In this work, two healthcare datasets, CMC and cancer, are used and the proposed algorithm obtains better results when compared to MEBB-BC, BB-BC, GA, PSO and K-means algorithms. The performance of the improved BB-BC algorithm is also examined against benchmark clustering datasets. The simulation results showed that proposed algorithm improves the clustering results significantly when compared to other algorithms.
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Affiliation(s)
- Hakam Singh
- Department of Computer Science and Engineering, Jaypee University of Information Technology Waknaghat, Solan, Himachal Pradesh, India
| | - Yugal Kumar
- Department of Computer Science and Engineering, Jaypee University of Information Technology Waknaghat, Solan, Himachal Pradesh, India
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Mustafa HMJ, Ayob M, Nazri MZA, Kendall G. An improved adaptive memetic differential evolution optimization algorithms for data clustering problems. PLoS One 2019; 14:e0216906. [PMID: 31137034 PMCID: PMC6538400 DOI: 10.1371/journal.pone.0216906] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 04/30/2019] [Indexed: 11/23/2022] Open
Abstract
The performance of data clustering algorithms is mainly dependent on their ability to balance between the exploration and exploitation of the search process. Although some data clustering algorithms have achieved reasonable quality solutions for some datasets, their performance across real-life datasets could be improved. This paper proposes an adaptive memetic differential evolution optimisation algorithm (AMADE) for addressing data clustering problems. The memetic algorithm (MA) employs an adaptive differential evolution (DE) mutation strategy, which can offer superior mutation performance across many combinatorial and continuous problem domains. By hybridising an adaptive DE mutation operator with the MA, we propose that it can lead to faster convergence and better balance the exploration and exploitation of the search. We would also expect that the performance of AMADE to be better than MA and DE if executed separately. Our experimental results, based on several real-life benchmark datasets, shows that AMADE outperformed other compared clustering algorithms when compared using statistical analysis. We conclude that the hybridisation of MA and the adaptive DE is a suitable approach for addressing data clustering problems and can improve the balance between global exploration and local exploitation of the optimisation algorithm.
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Affiliation(s)
- Hossam M. J. Mustafa
- Data Mining and Optimization Research Group, Center of Artificial Intelligence Technology, Faculty of Information Science and Technology, University Kebangsaan Malaysia, Bangi, Malaysia
- * E-mail:
| | - Masri Ayob
- Data Mining and Optimization Research Group, Center of Artificial Intelligence Technology, Faculty of Information Science and Technology, University Kebangsaan Malaysia, Bangi, Malaysia
| | - Mohd Zakree Ahmad Nazri
- Data Mining and Optimization Research Group, Center of Artificial Intelligence Technology, Faculty of Information Science and Technology, University Kebangsaan Malaysia, Bangi, Malaysia
| | - Graham Kendall
- ASAP Research Group, University of Nottingham Malaysia, Malaysia
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