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# kmeans()

Description:

Perform an unsupervised clustering algorithm that divides a dataset into predetermined number of clusters based on the minimum error function.

Syntax:

 kmeans(A,k) Perform training on training set A using training parameter k, and return training result model R kmeans(R,B) Perform prediction on scoring set B according to model R and return the prediction result kmeans(A,k,B) Connect training and prediction; perform a linkage task of model training and data scoring by inputting training data A, training parameter k and scoring data B, and return the prediction result

Note:

The external library function (See External Library Guide) performs an unsupervised clustering algorithm that divides a dataset into predetermined number of clusters based on the minimum error function.

Parameter:

 A A sequence, which is the training set k An integer, which is the number of clusters; support 2 only R A sequence, which is the result returned by syntax kmeans(A,k) B A sequence, which is the scoring set

Return value:

Sequence

Example:

 A 1 [[1,2,3,4],[2,3,1,2],[1,1,1,-1],[1,0,-2,-6]] Training set A. 2 2 Parameter k. 3 [[6,2,3,5],[0,3,1,5],[1,2,1,-1],[1,5,2,-6]] Scoring set B. 4 =kmeans(A1,A2) Perform training on A1 according to k=2 and return training result R. 5 =kmeans(A4,A3) Perform data prediction on the scoring set using A4’s training result R and return the prediction result; separate A3’s sample into two groups, where sample 1 and sample 2 are in the same group and sample 3 and sample 4 are put another group. 6 =kmeans(A1,A2,A3) Input the training set, parameter k and scoring set to perform training and scoring in a row, and return prediction result, which is the same as A5.