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).
morlini(data)data frame or matrix with cases in rows and variables in colums. Cases are characterized by nominal (categorical) variables coded as numbers.
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.
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. Available at: http://www-users.cs.umn.edu/~sboriah/PDFs/BoriahBCK2008.pdf.
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.
eskin,
good1,
good2,
good3,
good4,
iof,
lin,
lin1,
of,
sm,
ve,
vm.
#sample data
data(data20)
# Creation of proximity matrix
prox_morlini <- morlini(data20)
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