SYAZILI, MUHAMMAD and Jambak, M. Ihsan and Efendi, Rusdi (2020) PENGARUH METODE PARTICLE SWARM OPTIMIZATION DALAM PENENTUAN CENTROID AWAL TERHADAP KUALITAS HASIL CLUSTERING ALGORITMA K-MEANS. Undergraduate thesis, Sriwijaya University.
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Abstract
The quality of clustering results using k-means depend on initialization of early centroid. Initialization of early centroid generated randomly, produce in a convergen condition such a local optimum, therefore it needs to be found that k-means algorithm be able to produce in a convergen condition such a global optimum using Particle Swarm Optimization. In addition, most of clustering algorithm works well in handling low dimentional data and low dimentions can be achieved by doing dimentional reduction. From this research, the determination of early centroid using Particle Swarm Optimization can improve the quality of clustering results using k-means compared to the determination of early centroid randomly without dimentional reduction by showing a significant decrease in the DBI value of 31,61818134108936%. Meanwhile, the determination of early centroid using Particle Swarm Optimization can improve the quality of clustering results using k-means compared to determination of early centroid radomly with dimentional reduction by showing significant decrease in the DBI value of 38,5403727707036%.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Algoritma K-Means, Penentuan Centroid Awal Algoritma K-Means, Particle Swarm Optimization, Reduksi Dimensi |
| Subjects: | Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. |
| Divisions: | 09-Faculty of Computer Science > 55201-Informatics (S1) |
| Depositing User: | Users 7772 not found. |
| Date Deposited: | 01 Sep 2020 07:30 |
| Last Modified: | 01 Sep 2020 07:30 |
| URI: | http://repository.unsri.ac.id/id/eprint/34317 |
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