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Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
| Content Provider | CiteSeerX |
|---|---|
| Author | Belhaouari, Samir Brahim |
| Abstract | 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. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Variable Nn New Cluster-k-nearest Neighbor Variable Data Size K-means Cluster Cluster-k-nearest Neighbor Subclass Number Accuracy Control Chart Pattern Recognition New Clustering Algorithm Mean Point K-means Cluster Algorithm |
| Content Type | Text |
