nomclust (version 1.1.1106)

lin: Lin Measure

Description

The Lin measure was introduced by Lin (1998). The measure assigns higher weights to more frequent categories in case of matches and lower weights to less frequent categories in case of mismatches. 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).

Usage

lin(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.

Lin, D. (1998). An information-theoretic definition of similarity. In: ICML '98: Proceedings of the 15th International Conference on Machine Learning. San Francisco, p. 296-304.

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, lin1, morlini, of, sm, ve, vm.

Examples

# NOT RUN {
#sample data
data(data20)
# Creation of proximity matrix
prox_lin <- lin(data20)

# }