International Journal of System Signal Control and Engineering Application
ISSN: OnlineISSN: Print 1997-5422
Abstract
In recent years, combinational optimization issues are introduced as critical problems in clustering algorithms to partition data in a way that optimizes the performance of clustering. K-means algorithm is one of the famous and more popular clustering algorithms which can be simply implemented and it can easily solve the optimization issue with less extra information. In this regard, researchers have worked to improve the problem computationally, creating efficient solutions that lead to better data analysis through the K-means Clustering algorithm. Finally, the Partial Swarm Optimization (GAPSO) and Partial Swarm Optimization-Genetic Algorithm (PSOGA) through the K-means algorithm were proposed.
How to cite this article:
K. Jahanbin, A. Afroozeh and Y. Farhang. Improved Particle Swarm Optimization Algorithm in K-Means.
DOI: https://doi.org/10.36478/ijssceapp.2017.41.47
URL: https://www.makhillpublications.co/view-article/1997-5422/ijssceapp.2017.41.47