<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/9707" mdate="2009-01-28 00:00:00"> <author>Samir Brahim Belhaouari</author> <title>Fast and Accuracy Control Chart Pattern Recognition using a New clusterkNearest Neighbor</title> <pages>52 - 56</pages> <year>2009</year> <volume>3</volume> <number>1</number> <journal>International Journal of Mathematical and Computational Sciences</journal> <ee>https://publications.waset.org/pdf/9707</ee> <url>https://publications.waset.org/vol/25</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>By taking advantage of both kNN which is highly accurate and Kmeans cluster which is able to reduce the time of classification, we can introduce ClusterkNearest Neighbor as &amp;quot;variable k&amp;quot;NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of Kmeans 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 Kmeans 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 Kmeans cluster algorithm and we find 99.7 by using the new clustering algorithm.</abstract> <index>Open Science Index 25, 2009</index> </article>