TY - JFULL
AU - Samir Brahim Belhaouari
PY - 2009/2/
TI - Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
T2 - International Journal of Mathematical and Computational Sciences
SP - 51
EP - 56
VL - 3
SN - 1307-6892
UR - https://publications.waset.org/pdf/9707
PU - World Academy of Science, Engineering and Technology
NX - Open Science Index 25, 2009
N2 - 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 lassification of 6 different types of Time series by using K-means cluster algorithm and we find 99.7% by using the new clustering algorithm.
ER -