Xiwu Han  , Tiejun Zhao  , 
    Auto-K Dynamic Clustering Algorithm,
    Asian Journal of Information Technology,
    Volume 4,Issue 4,
    2005,
    Pages 467-471,
    ISSN 1682-3915,
    ajit.2005.467.471,
    (https://makhillpublications.co/view-article.php?doi=ajit.2005.467.471)
    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.
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