@article{MAKHILLIJSSCEA201710128760,
    title = {Improved Particle Swarm Optimization Algorithm in K-Means},
    journal = {International Journal of System Signal Control and Engineering Application},
    volume = {10},
    number = {1},
    pages = {41-47},
    year = {2017},
    issn = {1997-5422},
    doi = {ijssceapp.2017.41.47},
    url = {https://makhillpublications.co/view-article.php?issn=1997-5422&doi=ijssceapp.2017.41.47},
    author = {K.,A. and},
    keywords = {clustering,Improved particle,K-means,swarm optimization,algorithm,PSOGA},
    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.}
    }