lin: Lin (LIN) Measure

In nomclust: Hierarchical Cluster Analysis of Nominal Data

Lin (LIN) Measure

Description

The function calculates a dissimilarity matrix based on the LIN similarity measure.

Usage

lin(data, var.weights = NULL)

Arguments

data	A data.frame or a matrix with cases in rows and variables in columns.
var.weights	A numeric vector setting weights to the used variables. One can choose the real numbers from zero to one.

Details

The Lin measure was introduced by Lin (1998) and presented in (Boriah et al., 2008). The measure assigns higher weights to more frequent categories in case of matches and lower weights to less frequent categories in case of mismatches.

Value

The function returns an object of the class "dist".

Author(s)

Zdenek Sulc.
Contact: [e-mail]

References

Boriah S., Chandola 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.

See Also

anderberg, burnaby, eskin, gambaryan, goodall1, goodall2, goodall3, goodall4, iof, lin1, of, sm, smirnov, ve, vm.

Examples

# sample data
data(data20)

# dissimilarity matrix calculation
prox.lin <- lin(data20)

# dissimilarity matrix calculation with variable weights
weights.lin<- lin(data20, var.weights = c(0.7, 1, 0.9, 0.5, 0))

nomclust documentation built on Aug. 18, 2023, 5:06 p.m.