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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| Format: | Citation Index Journal |
| Language: | English |
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Word Academy of Science
2009
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| Subjects: | |
| Online Access: | http://scholars.utp.edu.my/id/eprint/2719/ http://scholars.utp.edu.my/id/eprint/2719/1/C_K_NN.pdf |
| _version_ | 1848659292585984000 |
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| author | Brahim Belhaouari, samir |
| author_facet | Brahim Belhaouari, samir |
| author_sort | Brahim Belhaouari, samir |
| building | UTP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-13T07:28:07Z |
| format | Citation Index Journal |
| id | oai:scholars.utp.edu.my:2719 |
| institution | Universiti Teknologi Petronas |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-13T07:28:07Z |
| publishDate | 2009 |
| publisher | Word Academy of Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:scholars.utp.edu.my:27192017-01-19T08:25:30Z http://scholars.utp.edu.my/id/eprint/2719/ 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 en http://scholars.utp.edu.my/id/eprint/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] |
| spellingShingle | QA75 Electronic computers. Computer science Brahim Belhaouari, samir Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science |
| url | http://scholars.utp.edu.my/id/eprint/2719/ http://scholars.utp.edu.my/id/eprint/2719/1/C_K_NN.pdf |