R: Modification of Carmone, Kara & Maxwell Heuristic...
HINoV.Symbolic
R Documentation
Modification of Carmone, Kara & Maxwell Heuristic Identification of Noisy Variables (HINoV) method for symbolic interval data
Description
Modification of Heuristic Identification of Noisy Variables (HINoV) method for symbolic interval data
Usage
HINoV.Symbolic(x, u=NULL, distance="H", method = "pam",
Index = "cRAND")
Arguments
x
symbolic interval data: a 3-dimensional table, first dimension represents object number, second dimension - variable number, and third dimension contains lower- and upper-bounds of intervals
u
number of clusters
distance
"M" - minimal distance between all vertices of hyper-cubes defined by symbolic interval variables; "H" - Hausdorff distance; "S" - sum of squares of distance between all vertices of hyper-cubes defined by symbolic interval variables
m x m symmetric matrix (m - number of variables). Matrix contains pairwise corrected Rand (Rand) indices for partitions formed by the j-th variable with partitions formed by the l-th variable
Carmone, F.J., Kara, A., Maxwell, S. (1999), HINoV: a new method to improve market segment definition by identifying noisy variables, "Journal of Marketing Research", November, vol. 36, 501-509.
Hubert, L.J., Arabie, P. (1985), Comparing partitions, "Journal of Classification", no. 1, 193-218.
Rand, W.M. (1971), Objective criteria for the evaluation of clustering methods, "Journal of the American Statistical Association", no. 336, 846-850.
Walesiak, M., Dudek, A. (2008), Identification of noisy variables for nonmetric and symbolic data in cluster analysis, In: C. Preisach, H. Burkhardt, L. Schmidt-Thieme, R. Decker (Eds.), Data analysis, machine learning and applications, Springer-Verlag, Berlin, Heidelberg, 85-92.