RKMeans: Regularized K-means clustering algorithm for multi-view data

In ORKM: The Online Regularized K-Means Clustering Algorithm

Regularized K-means clustering algorithm for multi-view data

Description

This function improves the regularized K-means clustering (RKMC) algorithm for the multi-view data clustering problem. Specifically, the regularisation term is added to the K-means algorithm to avoid overfitting of the data. Numerical analysis shows that the RKMC algorithm significantly improves the clustering performance compared to other methods. In addition, in order to reveal the structure of real data as realistically as possible, improve the clustering accuracy of high-dimensional data, and balance the weights of each view, the RKMC algorithm assigns a series of learnable weight values to each view, thus reflecting the relationship and compatibility of each view more flexibly.

Usage

RKMeans(X, K, V, yita, r, max.iter, truere, method = 0)

Arguments

X	is the data matrix
K	is the number of cluster
V	is the view of X
yita	is the regularized parameter
r	is the banlance parameter
max.iter	is the max iter
truere	is the ture label in data set
method	is the caluate the NMI

Value

NMI,weight,center,result

Author(s)

Miao Yu

Examples

  library(MASS) 
  library(Matrix)  
  yita=0.5;V=2;K=3;r=0.5;max.iter=10;n1=n2=n3=70
  X1<-rnorm(n1,20,2);X2<-rnorm(n2,25,1.5);X3<-rnorm(n3,30,2) 
  Xv<-c(X1,X2,X3)
  data<-matrix(Xv,n1+n2+n3,2)
  data[1:70,2]<-1;data[71:140,2]<-2;data[141:210,2]<-3
  X<-matrix(data[,1],n1+n2+n3,1) 
  truere=data[,2]
  lamda1<-0.2;lamda2<-0.8
  lamda<-matrix(c(lamda1,lamda2),nrow=1,ncol=2)
  sol.svd <- svd(lamda)
  U1<-sol.svd$u
  D1<-sol.svd$d
  V1<-sol.svd$v
  C1<-t(U1)
  Y1<-C1/D1
  view<-V1
  view1<-matrix(view[1,])
  view2<-matrix(view[2,])
  X1<-matrix(view1,n1+n2+n3,1)
  X2<-matrix(view2,n1+n2+n3,1)
  RKMeans(X=X1,K=K,V=V,yita=yita,r=r,max.iter=max.iter,truere=truere,method=0)

ORKM documentation built on April 16, 2025, 5:08 p.m.