An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm
Among the data clustering algorithms, k-means (KM) algorithm is one of the most popular clustering techniques due to its simplicity and efficiency. However, k-means is sensitive to initial centers and it has the local optima problem. K-harmonic-means (KHM) clustering algorithm solves the initializat...
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| Format: | Article |
| Language: | English |
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Sciendo
2016-06-01
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| Series: | Foundations of Computing and Decision Sciences |
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| Online Access: | https://doi.org/10.1515/fcds-2016-0006 |
| _version_ | 1798039717917229056 |
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| author | Bouyer Asgarali |
| author_facet | Bouyer Asgarali |
| author_sort | Bouyer Asgarali |
| collection | DOAJ |
| description | Among the data clustering algorithms, k-means (KM) algorithm is one of the most popular clustering techniques due to its simplicity and efficiency. However, k-means is sensitive to initial centers and it has the local optima problem. K-harmonic-means (KHM) clustering algorithm solves the initialization problem of k-means algorithm, but it also has local optima problem. In this paper, we develop a new algorithm for solving this problem based on an improved version of particle swarm optimization (IPSO) algorithm and KHM clustering. In the proposed algorithm, IPSO is equipped with Cuckoo Search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence and escape from local optima. IPSO updates positions of particles based on a combination of global worst, global best with personal worst and personal best to dynamically be used in each iteration of the IPSO. The experimental result on five real-world datasets and two artificial datasets confirms that this improved version is superior to k-harmonic means and regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy and correctness while markedly improving the processing time. |
| first_indexed | 2024-04-11T21:57:36Z |
| format | Article |
| id | doaj.art-871c93bd62e7453fb2d2ca6b2a9b24bf |
| institution | Directory Open Access Journal |
| issn | 2300-3405 |
| language | English |
| last_indexed | 2024-04-11T21:57:36Z |
| publishDate | 2016-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Foundations of Computing and Decision Sciences |
| spelling | doaj.art-871c93bd62e7453fb2d2ca6b2a9b24bf2022-12-22T04:01:02ZengSciendoFoundations of Computing and Decision Sciences2300-34052016-06-014129912110.1515/fcds-2016-0006fcds-2016-0006An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search AlgorithmBouyer Asgarali0Faculty of Information Technology and Communications, Azarbaijan Shahid Madani University, Tabriz, IranAmong the data clustering algorithms, k-means (KM) algorithm is one of the most popular clustering techniques due to its simplicity and efficiency. However, k-means is sensitive to initial centers and it has the local optima problem. K-harmonic-means (KHM) clustering algorithm solves the initialization problem of k-means algorithm, but it also has local optima problem. In this paper, we develop a new algorithm for solving this problem based on an improved version of particle swarm optimization (IPSO) algorithm and KHM clustering. In the proposed algorithm, IPSO is equipped with Cuckoo Search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence and escape from local optima. IPSO updates positions of particles based on a combination of global worst, global best with personal worst and personal best to dynamically be used in each iteration of the IPSO. The experimental result on five real-world datasets and two artificial datasets confirms that this improved version is superior to k-harmonic means and regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy and correctness while markedly improving the processing time.https://doi.org/10.1515/fcds-2016-0006k-meansk-harmonic means clusteringparticle swarm optimizationlévy flightlocal minimum |
| spellingShingle | Bouyer Asgarali An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm Foundations of Computing and Decision Sciences k-means k-harmonic means clustering particle swarm optimization lévy flight local minimum |
| title | An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm |
| title_full | An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm |
| title_fullStr | An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm |
| title_full_unstemmed | An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm |
| title_short | An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm |
| title_sort | optimized k harmonic means algorithm combined with modified particle swarm optimization and cuckoo search algorithm |
| topic | k-means k-harmonic means clustering particle swarm optimization lévy flight local minimum |
| url | https://doi.org/10.1515/fcds-2016-0006 |
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