nomclust (version 1.1.1106)

good2: Goodall 2 Measure

Description

The Goodall 2 similarity measure was firstly introduced in (Boriah et al., 2008). The measure assigns higher similarity to infrequent matches under condition that there are also other categories, which are even less frequent than the examined one. 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, 2015).

Usage

good2(data)

Arguments

data

data frame 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.

Goodall, V.D. (1966). A new similarity index based on probability. Biometrics, 22(4), p. 882.

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.

See Also

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

Examples

# NOT RUN {
#sample data
data(data20)
# Creation of proximity matrix
prox_goodall_2 <- good2(data20)

# }