TY  - JOUR
T1  - Improved Particle Swarm Optimization Algorithm in K-Means
AU - Jahanbin, K. AU - Afroozeh, A. AU - Farhang, Y. 
JO  - International Journal of System Signal Control and Engineering Application
VL  - 10
IS  - 1
SP  - 41
EP  - 47
PY  - 2017
DA  - 2001/08/19
SN  - 1997-5422
DO  - ijssceapp.2017.41.47
UR  - https://makhillpublications.co/view-article.php?doi=ijssceapp.2017.41.47
KW  - clustering
KW  -Improved particle
KW  -K-means
KW  -swarm optimization
KW  -algorithm
KW  -PSOGA
AB  - 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.
ER  -