DMC: Deep matrix clustering algorithm for multi-view data

In ORKM: The Online Regularized K-Means Clustering Algorithm

Deep matrix clustering algorithm for multi-view data

Description

This algorithm decomposes the multi-view data matrix into representative subspaces layer by layer, and generates a cluster at each layer. To enhance the diversity between the generated clusters, new redundant quantifiers arising from the proximity between samples in these subspaces are minimised. An iterative optimisation process is further introduced to simultaneously seek multiple clusters with quality and diversity.

Usage

DMC(X, K, V, r, lamda, truere, max.iter, method = 0)

Arguments

X	data matrix
K	number of cluster
V	number of view
r	first banlance parameter
lamda	second balance parameter
truere	true cluster result
max.iter	max iter
method	caculate the index of NMI

Value

NMI,Alpha1,center,result

Author(s)

Miao Yu

Examples

 library(MASS)   
 V=2;lamda=0.5;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
 truere=data[,2]
 X<-matrix(data[,1],n1+n2+n3,1) 
 lamda1<-0.2;lamda2<-0.8
 lamda0<-matrix(c(lamda1,lamda2),nrow=1,ncol=2)
 sol.svd <- svd(lamda0)
 U1<-sol.svd$u
 D1<-sol.svd$d
 V1<-sol.svd$v
 C1<-t(U1)%*%t(X)
 Y1<-C1/D1
 view<-V1%*%Y1
 view1<-matrix(view[1,])
 view2<-matrix(view[2,])
 X1<-matrix(view1,n1+n2+n3,1)
 X2<-matrix(view2,n1+n2+n3,1)
 DMC(X=X1,K=K,V=V,lamda=lamda,r=r,max.iter=max.iter,truere=truere,method=0)

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