## 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.

Original language | English |
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Pages (from-to) | 1142-1146 |

Number of pages | 5 |

Journal | World Academy of Science, Engineering and Technology |

Volume | 37 |

Publication status | Published - Jan 2009 |

Externally published | Yes |

## Keywords

- Classification
- Gaussian mixture model
- Pattern recognition
- Time series
- k-Nearest neighbor
- k-means cluster