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MODIFIED SELECTION OF INITIAL CENTROIDS FOR K- MEANS ALGORITHM
- Source :
- MATTER: International Journal of Science and Technology. 2:48-64
- Publication Year :
- 2017
- Publisher :
- Global Research & Development Services, 2017.
-
Abstract
- This study focuses on the improved initialization of initial centroids instead of random selection for the K-means algorithm. The random selection of initial seeds is a major drawback of the original Kmeans algorithm because it leads to less reliable result of clustering the data. The modified approach of the k-means algorithm integrates the computation of the weighted mean to improve the seeds initialization. This paper shows the comparison of K-Means and Modified K-Means algorithm, using the first simple dataset of four objects and the dataset for service vehicles. The two simple applications proved that the Modified K- Means of selecting initial centroids is more reliable than K-Means Algorithm. Clustering is better achieved in the modified k-means algorithm. Article DOI: http://dx.doi.org/10.20319/mijst.2016.22.4864 This work is licensed under the Creative Commons Attribution-Non-commercial 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
- Subjects :
- 0301 basic medicine
business.industry
Population-based incremental learning
k-means clustering
Initialization
Pattern recognition
03 medical and health sciences
030104 developmental biology
Ramer–Douglas–Peucker algorithm
Canopy clustering algorithm
Artificial intelligence
Cluster analysis
business
Selection (genetic algorithm)
FSA-Red Algorithm
Mathematics
Subjects
Details
- ISSN :
- 24545880
- Volume :
- 2
- Database :
- OpenAIRE
- Journal :
- MATTER: International Journal of Science and Technology
- Accession number :
- edsair.doi...........db465779d2ae26ee1e381953079da32a