%0 Journal Article
	%A Samir Brahim Belhaouari
	%D 2009
	%J International Journal of Mathematical and Computational Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 25, 2009
	%T Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
	%U https://publications.waset.org/pdf/9707
	%V 25
	%X By taking advantage of both k-NN which is highly
accurate and K-means cluster which is able to reduce the time of classification, we can introduce Cluster-k-Nearest Neighbor as "variable k"-NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less
subclass number, stability and bounded time of classification with respect to the variable data size. We find between 96% and 99.7 % of accuracy in the lassification of 6 different types of Time series by using K-means cluster algorithm and we find 99.7% by using the new clustering algorithm.
	%P 52 - 56