clustatis_kmeans {ClustBlock} | R Documentation |

Partitionning algorithm for quantitative variables. Each cluster is associated with a compromise computed by the STATIS method. Moreover, a noise cluster can be set up.

clustatis_kmeans(Data, Blocks, clust, nstart=100, rho=0, NameBlocks=NULL, Itermax=30,Graph_groups=TRUE, Graph_weights=FALSE, scale=FALSE, print_attempt=FALSE)

`Data` |
data frame or matrix. Correspond to all the blocks of variables merged horizontally |

`Blocks` |
numerical vector. The number of variables of each block. The sum must be equal to the number of columns of Data |

`clust` |
numerical vector or integer. Initial partition or number of starting partitions if integer. If numerical vector, the numbers must be 1,2,3,...,number of clusters |

`nstart` |
integer. Number of starting partitions. Default: 100 |

`rho` |
numerical between 0 and 1. Threshold for the noise cluster. Default:0 |

`NameBlocks` |
string vector. Name of each block. Length must be equal to the length of Blocks vector. If NULL, the names are B1,...Bm. Default: NULL |

`Itermax` |
numerical. Maximum of iterations by partitionning algorithm. Default: 30 |

`Graph_groups` |
logical. Should each cluster compromise be plotted? Default: TRUE |

`Graph_weights` |
logical. Should the barplot of the weights in each cluster be plotted? Default: FALSE |

`scale` |
logical. Should the data variables be scaled? Default: FALSE |

`print_attempt` |
logical. Print the number of remaining attempts in the multi-start case? Default: FALSE |

a list with:

group: the clustering partition. If rho>0, some blocks could be in the noise cluster ("K+1")

rho: the threshold for the noise cluster

homogeneity: percentage of homogeneity of the blocks in each cluster and the overall homogeneity

rv_with_compromise: RV coefficient of each block with its cluster compromise

weights: weight associated with each block in its cluster

comp_RV: RV coefficient between the compromises associated with the various clusters

compromise: the W compromise of each cluster

coord: the coordinates of objects of each cluster

inertia: percentage of total variance explained by each axis for each cluster

rv_all_cluster: the RV coefficient between each block and each cluster compromise

criterion: the CLUSTATIS criterion error

param: parameters called

type: parameter passed to other functions

Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2018). Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods. Application to sensometrics. Food Quality and Preference, in Press.

Llobell, F., Vigneau, E., Qannari, E. M. (2019). Clustering datasets by means of CLUSTATIS with identification of atypical datasets. Application to sensometrics. Food Quality and Preference, 75, 97-104.

`plot.clustatis`

, `clustatis`

, `summary.clustatis`

, `statis`

data(smoo) NameBlocks=paste0("S",1:24) #with multi-start cl_km=clustatis_kmeans(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks, clust=3) #with an initial partition cl=clustatis(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks, Graph_dend=FALSE) partition=cl$cutree_k$partition3 cl_km2=clustatis_kmeans(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks, clust=partition, Graph_weights=FALSE, Graph_groups=FALSE) graphics.off()

[Package *ClustBlock* version 2.4.0 Index]