@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}, }