Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis
In today’s data-driven world, we are constantly exposed to a vast amount of information. This information is stored in various information systems and is used for analysis and management purposes. One important approach to handle these data is through the process of clustering or categorization. Clu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2024/3795126 |
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| author | Abdel Fattah Azzam Ahmed Maghrabi Eman El-Naqeeb Mohammed Aldawood Hewayda ElGhawalby |
| author_facet | Abdel Fattah Azzam Ahmed Maghrabi Eman El-Naqeeb Mohammed Aldawood Hewayda ElGhawalby |
| author_sort | Abdel Fattah Azzam |
| collection | DOAJ |
| description | In today’s data-driven world, we are constantly exposed to a vast amount of information. This information is stored in various information systems and is used for analysis and management purposes. One important approach to handle these data is through the process of clustering or categorization. Clustering algorithms are powerful tools used in data analysis and machine learning to group similar data points together based on their inherent characteristics. These algorithms aim to identify patterns and structures within a dataset, allowing for the discovery of hidden relationships and insights. By partitioning data into distinct clusters, clustering algorithms enable efficient data exploration, classification, and anomaly detection. In this study, we propose a novel centroid-based clustering algorithm, namely, the morphological accuracy clustering algorithm (MAC algorithm). The proposed algorithm uses a morphological accuracy measure to define the centroid of the cluster. The empirical results demonstrate that the proposed algorithm achieves a stable clustering outcome in fewer iterations compared to several existing centroid-based clustering algorithms. Additionally, the clusters generated by these existing algorithms are highly susceptible to the initial centroid selection made by the user. |
| format | Article |
| id | doaj-art-88ef3ae4aba246dfa3be88909a82ff16 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-88ef3ae4aba246dfa3be88909a82ff162025-02-03T05:56:56ZengWileyApplied Computational Intelligence and Soft Computing1687-97322024-01-01202410.1155/2024/3795126Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster AnalysisAbdel Fattah Azzam0Ahmed Maghrabi1Eman El-Naqeeb2Mohammed Aldawood3Hewayda ElGhawalby4Department of MathematicsDepartment of MathematicsDepartment of Physics and Engineering MathematicsDepartment of MathematicsDepartment of Physics and Engineering MathematicsIn today’s data-driven world, we are constantly exposed to a vast amount of information. This information is stored in various information systems and is used for analysis and management purposes. One important approach to handle these data is through the process of clustering or categorization. Clustering algorithms are powerful tools used in data analysis and machine learning to group similar data points together based on their inherent characteristics. These algorithms aim to identify patterns and structures within a dataset, allowing for the discovery of hidden relationships and insights. By partitioning data into distinct clusters, clustering algorithms enable efficient data exploration, classification, and anomaly detection. In this study, we propose a novel centroid-based clustering algorithm, namely, the morphological accuracy clustering algorithm (MAC algorithm). The proposed algorithm uses a morphological accuracy measure to define the centroid of the cluster. The empirical results demonstrate that the proposed algorithm achieves a stable clustering outcome in fewer iterations compared to several existing centroid-based clustering algorithms. Additionally, the clusters generated by these existing algorithms are highly susceptible to the initial centroid selection made by the user.http://dx.doi.org/10.1155/2024/3795126 |
| spellingShingle | Abdel Fattah Azzam Ahmed Maghrabi Eman El-Naqeeb Mohammed Aldawood Hewayda ElGhawalby Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis Applied Computational Intelligence and Soft Computing |
| title | Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis |
| title_full | Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis |
| title_fullStr | Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis |
| title_full_unstemmed | Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis |
| title_short | Morphological Accuracy Data Clustering: A Novel Algorithm for Enhanced Cluster Analysis |
| title_sort | morphological accuracy data clustering a novel algorithm for enhanced cluster analysis |
| url | http://dx.doi.org/10.1155/2024/3795126 |
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