@article{MAKHILLJEAS2019141818429,
    title = {New Modified Dynamic Clustering Algorithm},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {18},
    pages = {6742-6746},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.6742.6746},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.6742.6746},
    author = {Mohamed and},
    keywords = {Dynamic clustering,optimal clustering,k-means algorithm,clustering quality,weather data,optimal
number},
    abstract = {k-clustering is one of the most common ways to divide the extracted data into clusters which is
considered a type of knowledge discovery. While there is a great research effort to determine the key features
of mass K, further investigation is needed to determine whether the optimal number of clusters can be found
during the process based on the cluster quality scale. This study presents a modified k-means algorithm used
to improve cluster quality and optimizing the optimal number of clusters. The k-means algorithm takes the
number of clusters (k) as input from the user. But in the practical scenario, it is difficult to determine the number
of clusters in advance. The evolution of the proposed method is equivalent to finding the value of the
threshold. The suggested threshold value will be used as a distance between the center of each group and other
group’s centers. Applying the modified algorithm improves the results of enter cluster is 0.111 and entra cluster
is 0.0034.}
    }