@article{MAKHILLAJIT2005444900,
    title = {Auto-K Dynamic Clustering Algorithm},
    journal = {Asian Journal of Information Technology},
    volume = {4},
    number = {4},
    pages = {467-471},
    year = {2005},
    issn = {1682-3915},
    doi = {ajit.2005.467.471},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2005.467.471},
    author = {Xiwu Han and},
    keywords = {},
    abstract = {Most clustering methods need a pre-determined clustering number or a certain similarity threshold, which makes them dependent on heuristic knowledge. The X-means method tries to estimate the number of clusters but only converges locally. This paper presents a novel and simple clustering algorithm named as Auto-K after its descriptive parent-algorithm-K-means, though Auto-K theory can be generalized beyond certain given deriving algorithms. In Auto-K, the algorithm itself automatically selects a globally optimal clustering number for the involved population, by maximizing the clustering fitness and thus the clustering process can be said to be really dynamic and most accordant with human`s common sense in clustering.}
    }