Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor

By taking advantage of both k-NN which is highly accurate and K-means cluster which
is able to reduce the time of classi¯cation, 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 classi¯cation with respect to the variable data size. We ¯nd between 96% and 99.7 % of
accuracy in the classi¯cation of 6 di®erent types of Time series by using K-means cluster
algorithm and we ¯nd 99.7% by using the new clustering algorithm.