Volume 21, Issue 3 (9-2024)
jor 2024, 21(3): 99-116 |
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Khadem M, Toloie Eshlaghy A, Fathi Hafshejani K. Presenting a New Meta-Heuristic Algorithm (Qashqai Optimization Algorithm) to Improve the Accuracy of Data Clustering Using the K-Means Method. jor 2024; 21 (3) :99-116
URL: http://jamlu.liau.ac.ir/article-1-1987-en.html
URL: http://jamlu.liau.ac.ir/article-1-1987-en.html
Department of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran. , AToloieEshlaghy@gmail.com
Abstract: (768 Views)
Clustering or cluster analysis is an unsupervised learning method that is often used as a data analysis method to discover interesting patterns in data such as customer groups based on their behavior. Since clustering is an NP-hard problem, it is useful to use evolutionary intelligence algorithms because of its success in solving a wide range of NP-hard problems. Many heuristic and meta-heuristic algorithms have been proposed to solve the clustering problem. The K-means method is the simplest method for data clustering, which has the advantages of speed and ease of use, and one of its disadvantages is the local optimal convergence. In this paper, after defining the objective function of minimizing the K-means algorithm using Qashqai meta-heuristic algorithm, it was implemented in Matlab software.
In designing the Qashqai algorithm, the characteristics of population-oriented, routing, memory-oriented, have been used to improve its performance in achieving the optimal global solution. The results of the proposed hybrid algorithm were compared with other popular algorithms and the results of the hypothesis test showed that the proposed algorithm is effective in achieving the desired answers.
In designing the Qashqai algorithm, the characteristics of population-oriented, routing, memory-oriented, have been used to improve its performance in achieving the optimal global solution. The results of the proposed hybrid algorithm were compared with other popular algorithms and the results of the hypothesis test showed that the proposed algorithm is effective in achieving the desired answers.
Keywords: Optimization, NP-Hard Problems, Data Clustering, K-Means Algorithm, Meta-Heuristic Algorithms, Qashqai Algorithm
Type of Study: Research |
Subject:
General
Received: 2023/03/30 | Accepted: 2023/09/3 | Published: 2024/09/22
Received: 2023/03/30 | Accepted: 2023/09/3 | Published: 2024/09/22
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