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...

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Main Author: Brahim Belhaouari, samir
Format: Citation Index Journal
Published: Word Academy of Science 2009
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Online Access:http://eprints.utp.edu.my/2719/1/C_K_NN.pdf
http://eprints.utp.edu.my/2719/
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spelling my.utp.eprints.27192017-01-19T08:25:30Z Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor Brahim Belhaouari, samir QA75 Electronic computers. Computer science 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. Word Academy of Science 2009-01 Citation Index Journal PeerReviewed application/pdf http://eprints.utp.edu.my/2719/1/C_K_NN.pdf Brahim Belhaouari, samir (2009) Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor. [Citation Index Journal] http://eprints.utp.edu.my/2719/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Brahim Belhaouari, samir
Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
description 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.
format Citation Index Journal
author Brahim Belhaouari, samir
author_facet Brahim Belhaouari, samir
author_sort Brahim Belhaouari, samir
title Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
title_short Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
title_full Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
title_fullStr Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
title_full_unstemmed Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
title_sort fast and accuracy control chart pattern recognition using a new cluster-k-nearest neighbor
publisher Word Academy of Science
publishDate 2009
url http://eprints.utp.edu.my/2719/1/C_K_NN.pdf
http://eprints.utp.edu.my/2719/
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