An Improved Speeding K-Nearest Neighbor Classification Method
In the traditional K-nearest neighbor classification method, for each sample to be tested, it needs to calculate the distance between it and all the training samples, so the time complexity is high. To solve this problem, presents an improved speeding K-NN classification method based on clustering dividing, called SK-NN algorithm. Firstly, the training samples are divided by the K-means clustering, and the train-ing samples are divided into multiple subsets. Then the testing sample is belonged to which cluster by the center and radius, and the test-ing sample is clustered by K-NN on this sub set. The sub set size is smaller than the size of original training sample, so the distances number need to be calculated is decreased and the learning efficiency of model is improved.
NETL
NSTL
万方数据
维普
