nomclust (version 1.1.1106)

morlini: Morlini and Zani's Measure S2

Description

The S2 measure was proposed by Morlini and Zani (2012) and it is based on a transformed dataset, which contains only binary variables (dummy coding). 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

morlini(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.

Morlini, I., Zani, S. (2012). A new class of weighted similarity indices using polytomous variables. In Journal of Classification, 29(2), p. 199-226.

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, of, sm, ve, vm.

Examples

# NOT RUN {
#sample data
data(data20)
# Creation of proximity matrix
prox_morlini <- morlini(data20)

# }