cluscata_kmeans {ClustBlock} | R Documentation |

Partitionning of binary Blocks from a CATA experiment. Each cluster is associated with a compromise computed by the CATATIS method. Moreover, a noise cluster can be set up.

cluscata_kmeans(Data,nblo, clust, nstart=100, rho=0, NameBlocks=NULL, NameVar=NULL, Itermax=30, Graph_groups=TRUE, print_attempt=FALSE, Warnings=FALSE)

`Data` |
data frame or matrix where the blocks of binary variables are merged horizontally. If you have a different format, see |

`nblo` |
numerical. Number of blocks (subjects). |

`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` |
numerical. Number of starting partitions. Default: 100 |

`rho` |
numerical between 0 and 1. Threshold for the noise cluster. If 0, there is no noise cluster. Default: 0 |

`NameBlocks` |
string vector. Name of each block. Length must be equal to the number of blocks. If NULL, the names are S1,...Sm. Default: NULL |

`NameVar` |
string vector. Name of each variable (attribute, the same names for each subject). Length must be equal to the number of attributes. If NULL, the colnames of the first block are taken. Default: NULL |

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

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

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

`Warnings` |
logical. Display warnings about the fact that none of the subjects in some clusters checked an attribute or product? Default: FALSE |

a list with:

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

rho: the threshold for the noise cluster

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

s_with_compromise: Similarity coefficient of each subject with its cluster compromise

weights: weight associated with each subject in its cluster

compromise: The compromise of each cluster

CA: The correspondance analysis results on each cluster compromise (coordinates, contributions...)

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

s_all_cluster: the similarity coefficient between each subject and each cluster compromise

param: parameters called

criterion: the CLUSCATA criterion error

type: parameter passed to other functions

Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2019). A new approach for the analysis of data and the clustering of subjects in a CATA experiment. Food Quality and Preference, 72, 31-39.

Llobell, F., Giacalone, D., Labenne, A., Qannari, E.M. (2019). Assessment of the agreement and cluster analysis of the respondents in a CATA experiment. Food Quality and Preference, 77, 184-190.

`plot.cluscata`

, `summary.cluscata`

, `catatis`

, `cluscata`

, `change_cata_format`

data(straw) cl_km=cluscata_kmeans(Data=straw[,1:(16*40)], nblo=40, clust=3) plot(cl_km, Graph_groups=FALSE, Graph_weights = TRUE) summary(cl_km)

[Package *ClustBlock* version 2.4.0 Index]