The IOF (Inverse Occurrence Frequency) measure was originally constructed for the text mining,
see (Sparck-Jones, 1972), later, it was adjusted for categorical variables.
The measure assigns higher similarity to mismatches on less frequent values and vice versa.
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).
iof(data)data frame with cases in rows and variables in colums. Cases are characterized by nominal (categorical) variables coded as numbers.
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.
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. Available at: http://www-users.cs.umn.edu/~sboriah/PDFs/BoriahBCK2008.pdf.
Spark-Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. In Journal of Documentation, 28(1), 11-21. Later: Journal of Documentation, 60(5) (2002), 493-502.
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.
eskin,
good1,
good2,
good3,
good4,
lin,
lin1,
morlini,
of,
sm,
ve,
vm.
#sample data
data(data20)
# Creation of proximity matrix
prox_iof <- iof(data20)
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