R: Modification of Carmone, Kara & Maxwell Heuristic...

HINoV.Mod {clusterSim} | R Documentation

## Modification of Carmone, Kara \& Maxwell Heuristic Identification of Noisy Variables (HINoV) method

### Description

Modification of Heuristic Identification of Noisy Variables (HINoV) method

### Usage

    HINoV.Mod (x, type="metric", s = 2, u, distance=NULL,
    	method = "kmeans", Index ="cRAND")

### Arguments

`x` | data matrix
`type` | "metric" (default) - all variables are metric (ratio, interval), "nonmetric" - all variables are nonmetric (ordinal, nominal) or vector containing for each variable value "m"(metric) or "n"(nonmetric) for mixed variables (metric and nonmetric), e.g. type=c("m", "n", "n", "m")
`s` | for metric data only: 1 - ratio data, 2 - interval or mixed (ratio & interval) data
`u` | number of clusters (for metric data only)
`distance` | NULL for kmeans method (based on data matrix) and nonmetric data for ratio data: "d1" - Manhattan, "d2" - Euclidean, "d3" - Chebychev (max), "d4" - squared Euclidean, "d5" - GDM1, "d6" - Canberra, "d7" - Bray-Curtis for interval or mixed (ratio & interval) data: "d1", "d2", "d3", "d4", "d5"
`method` | NULL for nonmetric data clustering method: "kmeans" (default) , "single", "ward.D", "ward.D2", "complete", "average", "mcquitty", "median", "centroid", "pam"
`Index` | "cRAND" - corrected Rand index (default); "RAND" - Rand index

### Details

See file ../doc/HINoVMod_details.pdf for further details

### Value

`parim` | m x m symmetric matrix (m - number of variables). Matrix contains pairwise corrected Rand (Rand) indices for partitions formed by the j-th variable with partitions formed by the l-th variable
`topri` | sum of rows of `parim`
`stopri` | ranked values of `topri` in decreasing order

### Author(s)

Marek Walesiak marek.walesiak@ue.wroc.pl, Andrzej Dudek andrzej.dudek@ue.wroc.pl

Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://keii.ue.wroc.pl/clusterSim/

### References

Carmone, F.J., Kara, A., Maxwell, S. (1999), HINoV: a new method to improve market segment definition by identifying noisy variables, "Journal of Marketing Research", November, vol. 36, 501-509.

Hubert, L.J., Arabie, P. (1985), Comparing partitions, "Journal of Classification", no. 1, 193-218. Available at: doi: 10.1007/BF01908075.

Rand, W.M. (1971), Objective criteria for the evaluation of clustering methods, "Journal of the American Statistical Association", no. 336, 846-850. Available at: doi: 10.1080/01621459.1971.10482356.

Walesiak, M. (2005), Variable selection for cluster analysis - approaches, problems, methods, Plenary Session of the Committee on Statistics and Econometrics of the Polish Academy of Sciences, 15 March, Wroclaw.

Walesiak, M., Dudek, A. (2008), Identification of noisy variables for nonmetric and symbolic data in cluster analysis, In: C. Preisach, H. Burkhardt, L. Schmidt-Thieme, R. Decker (Eds.), Data analysis, machine learning and applications, Springer-Verlag, Berlin, Heidelberg, 85-92. Available at: doi: 10.1007/978-3-540-78246-9_11

### See Also

`hclust`, `kmeans`, `dist`, `dist.GDM`, `dist.BC`, `dist.SM`, `cluster.Sim`

### Examples

    # for metric data
    library(clusterSim)
    data(data_ratio)
    r1<- HINoV.Mod(data_ratio, type="metric", s=1, 4, method="kmeans",
         Index="cRAND")
    print(r1$stopri)
    plot(r1$stopri[,2],xlab="Variable number", ylab="topri",
    xaxt="n", type="b")
    axis(1,at=c(1:max(r1$stopri[,1])),labels=r1$stopri[,1])

    # for nonmetric data
    library(clusterSim)
    data(data_nominal)
    r2<- HINoV.Mod (data_nominal, type="nonmetric", Index = "cRAND")
    print(r2$stopri)
    plot(r2$stopri[,2], xlab="Variable number", ylab="topri",
    xaxt="n", type="b")
    axis(1,at=c(1:max(r2$stopri[,1])),labels=r2$stopri[,1])

    # for mixed data
    library(clusterSim)
    data(data_mixed)
    r3<- HINoV.Mod(data_mixed, type=c("m","n","m","n"), s=2, 3, distance="d1",
         method="complete", Index="cRAND")
    print(r3$stopri)
    plot(r3$stopri[,2], xlab="Variable number", ylab="topri",
    xaxt="n", type="b")
    axis(1,at=c(1:max(r3$stopri[,1])),labels=r3$stopri[,1])

[Package clusterSim version 0.49-2 Index]