nomclust (version 1.1.1106)

sm: Simple Matching Coefficient

Description

The simple matching coefficient (Sokal, 1958) represents the simplest way for measuring of similarity. It does not impose any weigts. By a given variable, it assigns value 1 in case of match and value 0 otherwise. Hierarchical clustering methods require a proximity (dissimilarity) matrix instead of a similarity matrix as an entry for the analysis; therefore, dissimilarity D is computed from similarity S according the equation 1/S-1.

The use and evaluation of clustering with this measure can be found e.g. in (Sulc and Rezankova, 2014) or (Sulc, 2015).

Usage

sm(data)

Arguments

data

data frame or matrix with cases in rows and variables in colums. Cases are characterized by nominal (categorical) variables coded as numbers.

Value

Function returns a matrix of the size n x n, where n is the number of objects in original data. The matrix contains proximities between all pairs of objects. It can be used in hierarchical cluster analyses (HCA), e.g. in agnes.

References

Boriah, S., Chandola and V., Kumar, V. (2008). Similarity measures for categorical data: A comparative evaluation. In: Proceedings of the 8th SIAM International Conference on Data Mining, SIAM, p. 243-254.

Sokal, R., Michener, C. (1958). A statistical method for evaluating systematic relationships. In: Science bulletin, 38(22), The University of Kansas.

Sulc, Z. (2015). Application of Goodall's and Lin's similarity measures in hierarchical clustering. In Sbornik praci vedeckeho seminare doktorskeho studia FIS VSE. Praha: Oeconomica, 2015, p. 112-118. Available at: http://fis.vse.cz/wp-content/uploads/2015/01/DD_FIS_2015_CELY_SBORNIK.pdf.

Sulc, Z. and Rezankova, H. (2014). Evaluation of recent similarity measures for categorical data. In: AMSE. Wroclaw: Wydawnictwo Uniwersytetu Ekonomicznego we Wroclawiu, p. 249-258. Available at: http://www.amse.ue.wroc.pl/papers/Sulc,Rezankova.pdf.

See Also

eskin, good1, good2, good3, good4, iof, lin, lin1, morlini, of, ve, vm.

Examples

# NOT RUN {
#sample data
data(data20)
# Creation of proximity matrix
prox_sm <- sm(data20)

# }