kmeans()

Description:

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

Syntax:

kmeans(A,k)

Perfrom 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

Parameters:

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:

A 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