R: Compare different partitions for a data set

compClust {clues} | R Documentation

## Compare different partitions for a data set

### Description

Compare different partitions for a data set based on agreement indices, average sihouette index and CH index.

### Usage

    compClust(y, memMat, disMethod = "Euclidean")

### Arguments

`y` | data matrix which is an R matrix object (for dimension > 1) or vector object (for dimension = 1) with rows being observations and columns being variables.
`memMat` | cluster membership matrix. Each column corresponds to a partition of the matrix `y`. The numbers of clusters for different partitions can be different. The cluster membership of a g-cluster data set should take values: 1, 2, …, g.
`disMethod` | specification of the dissimilarity measure. The available measures are “Euclidean” and “1-corr”.

### Value

`avg.s` | a vector of average sihouette indices for the different partitions in `memMat`.
`CH` | a vector of CH indices for the different partitions in `memMat`.
`Rand` | a matrix of Rand indices measuring the pair-wise agreement among the different partitions in `memMat`.
`HA` | a matrix of Hubert and Arabie's adjusted Rand indices measuring the pair-wise agreement among the different partitions in `memMat`.
`MA` | a matrix of Morey and Agresti's adjusted Rand indices measuring the pair-wise agreement among the different partitions in `memMat`.
`FM` | a matrix of Fowlkes and Mallows's indices measuring the pair-wise agreement among the different partitions in `memMat`.
`Jaccard` | a matrix of Jaccard indices measuring the pair-wise agreement among the different partitions in `memMat`.

### References

Calinski, R.B., Harabasz, J., (1974). A dendrite method for cluster analysis. Communications in Statistics, Vol. 3, pages 1-27.

Kaufman, L., Rousseeuw, P.J., (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.

Milligan, G.W. and Cooper, M.C. (1986) A study of the comparability of external criteria for hierarchical cluster analysis. Multivariate Behavioral Research 21, 441–458.

Wang, S., Qiu, W., and Zamar, R. H. (2007). CLUES: A non-parametric clustering method based on local shrinking. Computational Statistics & Data Analysis, Vol. 52, issue 1, pages 286-298.

### Examples

        # Maronna data set
        data(Maronna)
        # data matrix
        maronna <- Maronna$maronna
        # cluster membership
        maronna.mem <- Maronna$maronna.mem

        # partition by clues and kmeans
        res_CH <- clues(maronna, strengthMethod = "CH", quiet = TRUE)
        res_sil <- clues(maronna, strengthMethod = "sil", quiet = TRUE)
        res_km_HW <- kmeans(maronna, 4, algorithm = "Hartigan-Wong")
        res_km_L <- kmeans(maronna, 4, algorithm = "Lloyd")
        res_km_F <- kmeans(maronna, 4, algorithm = "Forgy")
        res_km_M <- kmeans(maronna, 4, algorithm = "MacQueen")

        memMat <- cbind(maronna.mem, res_CH$mem, res_sil$mem,
            res_km_HW$cluster, res_km_L$cluster,
            res_km_F$cluster, res_km_M$cluster)

        colnames(memMat) <- c("true", "clues_CH", "clues_sil",
            "km_HW", "km_L", "km_F", "km_M")

        res <- compClust(maronna, memMat)

        print(sapply(res, function(x) {round(x,1)}))

[Package clues version 0.6.2.2 Index]