Brahim Belhaouari, samir (2009) Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor. [Citation Index Journal]
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Abstract
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.
| Item Type: | Citation Index Journal |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| ID Code: | 2719 |
| Deposited By: | Dr Samir Brahim Belhaouari |
| Deposited On: | 12 Jun 2011 04:31 |
| Last Modified: | 19 Jan 2017 08:25 |
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Fast and Accuracy Control Chart Pattern Recognition using a
New cluster-k-Nearest Neighbor. (deposited 02 Apr 2010 02:14)
- Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor. (deposited 12 Jun 2011 04:31) [Currently Displayed]
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