publication . Other literature type . Article . 2009

Fast And Accuracy Control Chart Pattern Recognition Using A New Cluster-K-Nearest Neighbor

Samir Brahim Belhaouari;
Open Access English
  • Published: 28 Jan 2009
  • Publisher: Zenodo
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 differ...
Subjects
free text keywords: Pattern recognition, Time series, k-Nearest Neighbor, k-means cluster, Gaussian Mixture Model, Classification
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Zenodo
Other literature type . 2009
Provider: Datacite
Zenodo
Other literature type . 2009
Provider: Datacite
ZENODO
Article . 2009
Provider: ZENODO
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