@article{(Open Science Index):https://publications.waset.org/pdf/9707,
	  title     = {Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor},
	  author    = {Samir Brahim Belhaouari},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Mathematical and Computational Sciences},
	  volume    = {3},
	  number    = {1},
	  year      = {2009},
	  pages     = {52 - 56},
	  ee        = {https://publications.waset.org/pdf/9707},
	  url   	= {https://publications.waset.org/vol/25},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 25, 2009},
	}