Data Clustering Using Moth-Flame Optimization Algorithm
A k-means algorithm is a method for clustering that has already gained a wide range of acceptability. However, its performance extremely depends on the opening cluster centers. Besides, due to weak exploration capability, it is easily stuck at local optima. Recently, a new metaheuristic called Moth...
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| Format: | Article |
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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/12/4086 |
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| author | Tribhuvan Singh Nitin Saxena Manju Khurana Dilbag Singh Mohamed Abdalla Hammam Alshazly |
| author_facet | Tribhuvan Singh Nitin Saxena Manju Khurana Dilbag Singh Mohamed Abdalla Hammam Alshazly |
| author_sort | Tribhuvan Singh |
| collection | DOAJ |
| description | A k-means algorithm is a method for clustering that has already gained a wide range of acceptability. However, its performance extremely depends on the opening cluster centers. Besides, due to weak exploration capability, it is easily stuck at local optima. Recently, a new metaheuristic called Moth Flame Optimizer (MFO) is proposed to handle complex problems. MFO simulates the moths intelligence, known as transverse orientation, used to navigate in nature. In various research work, the performance of MFO is found quite satisfactory. This paper suggests a novel heuristic approach based on the MFO to solve data clustering problems. To validate the competitiveness of the proposed approach, various experiments have been conducted using Shape and UCI benchmark datasets. The proposed approach is compared with five state-of-art algorithms over twelve datasets. The mean performance of the proposed algorithm is superior on 10 datasets and comparable in remaining two datasets. The analysis of experimental results confirms the efficacy of the suggested approach. |
| first_indexed | 2024-03-10T10:26:38Z |
| format | Article |
| id | doaj.art-bb3fd51af86e4c3e92b5d4f645ff51ee |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T10:26:38Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bb3fd51af86e4c3e92b5d4f645ff51ee2023-11-22T00:00:36ZengMDPI AGSensors1424-82202021-06-012112408610.3390/s21124086Data Clustering Using Moth-Flame Optimization AlgorithmTribhuvan Singh0Nitin Saxena1Manju Khurana2Dilbag Singh3Mohamed Abdalla4Hammam Alshazly5Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha 751030, IndiaDepartment of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, IndiaDepartment of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, IndiaSchool of Engineering and Applied Sciences, Bennett University, Greater Noida 201310, IndiaDepartment of Mathematics, Faculty of Science, King Khalid University, Abha 62529, Saudi ArabiaFaculty of Computers and Information, South Valley University, Qena 83523, EgyptA k-means algorithm is a method for clustering that has already gained a wide range of acceptability. However, its performance extremely depends on the opening cluster centers. Besides, due to weak exploration capability, it is easily stuck at local optima. Recently, a new metaheuristic called Moth Flame Optimizer (MFO) is proposed to handle complex problems. MFO simulates the moths intelligence, known as transverse orientation, used to navigate in nature. In various research work, the performance of MFO is found quite satisfactory. This paper suggests a novel heuristic approach based on the MFO to solve data clustering problems. To validate the competitiveness of the proposed approach, various experiments have been conducted using Shape and UCI benchmark datasets. The proposed approach is compared with five state-of-art algorithms over twelve datasets. The mean performance of the proposed algorithm is superior on 10 datasets and comparable in remaining two datasets. The analysis of experimental results confirms the efficacy of the suggested approach.https://www.mdpi.com/1424-8220/21/12/4086data clusteringdata miningk-meansmoth flame optimizationmeta-heuristic |
| spellingShingle | Tribhuvan Singh Nitin Saxena Manju Khurana Dilbag Singh Mohamed Abdalla Hammam Alshazly Data Clustering Using Moth-Flame Optimization Algorithm Sensors data clustering data mining k-means moth flame optimization meta-heuristic |
| title | Data Clustering Using Moth-Flame Optimization Algorithm |
| title_full | Data Clustering Using Moth-Flame Optimization Algorithm |
| title_fullStr | Data Clustering Using Moth-Flame Optimization Algorithm |
| title_full_unstemmed | Data Clustering Using Moth-Flame Optimization Algorithm |
| title_short | Data Clustering Using Moth-Flame Optimization Algorithm |
| title_sort | data clustering using moth flame optimization algorithm |
| topic | data clustering data mining k-means moth flame optimization meta-heuristic |
| url | https://www.mdpi.com/1424-8220/21/12/4086 |
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