Belhaouari, S.B. (2009) Fast and accuracy control chart pattern recognition using a new cluster-k-nearest neighbor. World Academy of Science, Engineering and Technology, 37. pp. 1142-1146. ISSN 2010376X
Full text not available from this repository.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 classification of 6 different types of Time series by using K-means cluster algorithm and we find 99.7 by using the new clustering algorithm. © 2009 WASET.ORG.
Item Type: | Article |
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Additional Information: | cited By 4 |
Uncontrolled Keywords: | Accuracy control; Classification; Cluster algorithms; Gaussian mixture model; K-means; k-means cluster; k-Nearest neighbor; K-nearest neighbors; Variable data, Communication channels (information theory); Object recognition; Time series, Clustering algorithms |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 09 Nov 2023 15:48 |
Last Modified: | 09 Nov 2023 15:48 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/783 |