{"title":"Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor","authors":"Samir Brahim Belhaouari","country":null,"institution":"","volume":25,"journal":"International Journal of Mathematical and Computational Sciences","pagesStart":52,"pagesEnd":57,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/9707","abstract":"By taking advantage of both k-NN which is highly\r\naccurate 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\r\nsubclass 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.","references":null,"publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 25, 2009"}