| eskin {nomclust} | R Documentation |
Eskin (ES) Measure
Description
The function calculates a dissimilarity matrix based on the ES similarity measure.
Usage
eskin(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 Eskin similarity measure was proposed by Eskin et al. (2002) and examined by Boriah et al., (2008). It is constructed to assign higher weights to mismatches on variables with more categories.
Value
The function returns an object of the class "dist".
Author(s)
Zdenek Sulc.
Contact: zdenek.sulc@vse.cz
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.
Eskin E., Arnold A., Prerau M., Portnoy L. and Stolfo S. (2002). A geometric framework for unsupervised anomaly detection.
In D. Barbara and S. Jajodia (Eds): Applications of Data Mining in Computer Security, p. 78-100. Norwell: Kluwer Academic Publishers.
See Also
anderberg,
burnaby,
gambaryan,
goodall1,
goodall2,
goodall3,
goodall4,
iof,
lin,
lin1,
of,
sm,
smirnov,
ve,
vm.
Examples
# sample data
data(data20)
# dissimilarity matrix calculation
prox.eskin <- eskin(data20)
# dissimilarity matrix calculation with variable weights
weights.eskin <- eskin(data20, var.weights = c(0.7, 1, 0.9, 0.5, 0))