TY  - JOUR
T1  - Auto-K Dynamic Clustering Algorithm
AU - , Xiwu Han AU - , Tiejun Zhao 
JO  - Asian Journal of Information Technology
VL  - 4
IS  - 4
SP  - 467
EP  - 471
PY  - 2005
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2005.467.471
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2005.467.471
KW  - 
AB  - 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.
ER  -