Description: k-means (Euclidean distance) clustering algorithm is the most basic clustering algorithm, is understanding and the basis for the application of clustering algorithm, through the k-means (Euclidean distance) clustering algorithm we are able to a preliminary understanding of the principles of data mining .
- [MyKmeans] - achieving K-mean clustering algorithms :
- [Bayesianidentification.Rar] - a very useful identification of MATLAB B
- [pca] - ppt Report of the PCA, PCA theory, the t
- [K_average] - Dynamic K-Means clustering algorithm sou
- [shibie] - Matlab achieved using several methods of
- [Students] - This is compiled with VC++ of student ac
- [ch3example1B] - Elliptic filter, fp = 2400hz, fs = 5000h
- [KMEANS] - K-Means dynamic algorithm source data mi
- [CalcVecDistance] - vc+ opencv calculating Euclidean distanc
- [MyTuXing2] - Image processing platform, using VC++ to
File list (Check if you may need any files):
k_means
.......\algorithm.cpp
.......\algorithm.h
.......\Debug
.......\.....\algorithm.obj
.......\.....\k_means algorithm.obj
.......\.....\k_means.exe
.......\.....\k_means.ilk
.......\.....\k_means.pch
.......\.....\k_means.pdb
.......\.....\vc60.idb
.......\.....\vc60.pdb
.......\k_means algorithm.cpp
.......\k_means.dsp
.......\k_means.dsw
.......\k_means.ncb
.......\k_means.opt
.......\k_means.plg
.......\聚类过程结果显示.txt
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