ORKM-package {ORKM}R Documentation

The Online Regularized K-Means Clustering Algorithm

Description

Algorithm of online regularized k-means to deal with online multi(single) view data. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.

Details

The DESCRIPTION file:

Package: ORKM
Title: The Online Regularized K-Means Clustering Algorithm
Date: 2024-5-5
Version: 0.8.0.0
Authors@R: c(person("Guangbao", "Guo",role = c("aut", "cre"), email = "ggb11111111@163.com", comment = c(ORCID = "0000-0002-4115-6218")), person("Miao", "Yu", role="aut"), person("Haoyue", "Song", role="aut"), person("Ruiling", "Niu", role="aut"))
Description: Algorithm of online regularized k-means to deal with online multi(single) view data. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02331888.2020.1823979>.
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.0
Author: Guangbao Guo [aut, cre] (0000-0002-4115-6218), Miao Yu [aut], Haoyue Song [aut], Ruiling Niu [aut]
Maintainer: Guangbao Guo <ggb11111111@163.com>
Suggests: testthat (>= 3.0.0)
Imports: MASS, Matrix, stats,
Config/testthat/edition: 3

Index of help topics: 

DMC                     Deep matrix clustering algorithm for multi-view
                        data
INDEX                   Caculate the indication on the functions
KMeans                  K-means clustering algorithm for multi/single
                        view data
OGD                     Online gradient descent algorithm for online
                        single-view data clustering
OMU                     Online multiplicative update algorithm for
                        online multi-view data clustering
ORKM-package            The Online Regularized K-Means Clustering
                        Algorithm
ORKMeans                Online regularized K-means clustering algorithm
                        for online multi-view data
PKMeans                 Power K-means clustering algorithm for single
                        view data
QCM                     The QCM data set with K=5.
RKMeans                 Regularized K-means clustering algorithm for
                        multi-view data
Washington_cites        The third view of Washington data set.
Washington_content      The second view of Washington data set.
Washington_inbound      The third view of Washington data set.
Washington_outbound     The fourth view of Washington data set.
Wisconsin_cites         The first view of Wisconsin data set.
Wisconsin_content       The second view of Wisconsin data set.
Wisconsin_inbound       The third view of Wisconsin data set.
Wisconsin_outbound      The fourth view of Wisconsin data set.
cora_view1              The first view of Cora data set.
cora_view2              The second view of Cora data set.
cora_view3              The third view of Cora data set.
cora_view4              The fourth view of Cora data set.
cornell_cites           The first view of Cornell data set.
cornell_content         The second view of Cornell data set.
cornell_inbound         The third view of Cornell data set.
cornell_outbound        The fourth view of Cornell data set.
labelTexas              True clustering labels for Texas data set.
labelWashington         True clustering labels for Washington data set.
labelWisconsin          True clustering labels for Wisconsin data set.
labelcora               True clustering labels for Cora data set.
labelcornell            True clustering labels for Cornell data set.
movie_1                 The first view of Movie data set.
movie_2                 The second view of Movie data set.
seed                    A single-view data set named Seeds.
sobar                   A single-view data set named Sobar.
texas_cites             The first view of Texas data set.
texas_content           The second view of Texas dataset.
texas_inbound           The third view of Texas data set.
texas_outbound          The fourth view of Texas data set.
turelabel               Ture label of Movie data set.

You can use this package for online multi-view clustering, the dataset and real labels are also provided in the package.

Author(s)

Guangbao Guo [aut, cre] (0000-0002-4115-6218), Miao Yu [aut], Haoyue Song [aut], Ruiling Niu [aut]

Maintainer: Guangbao Guo <ggb11111111@163.com>

References

Guangbao Guo, Miao Yu, Guoqi Qian, (2023), Orkm: Online Regularized k-Means Clustering for Online Multi-View Data.

See Also

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4484209

Examples

library(MASS) 
library(Matrix)  
  yita=0.5;V=2;chushi=100;K=3;r=0.5;max.iter=10;n1=n2=n3=70;gamma=0.1;alpha=0.98;epsilon=1
  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
  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)
  ORKMeans(X=X1,K=K,V=V,r=r,chushi=chushi,yita=yita,gamma=gamma,epsilon=epsilon,
max.iter=max.iter,truere=truere,method=0)
[Package ORKM version 0.8.0.0 Index]