MV.conceptClosestFit: Concept Closest Fit
In janusza/RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

Description Usage Arguments Details Value Author(s) References See Also Examples

It is used for handling missing values based on the concept closest fit.

1	MV.conceptClosestFit(decision.table)

decision.table

a "DecisionTable" class representing a decision table. See SF.asDecisionTable. Note: missing values are recognized as NA.

This method is similar to the global closest fit method. The difference is that the original data set, containing missing attribute values, is first split into smaller data sets, each smaller data set corresponds to a concept from the original data set. More precisely, every smaller data set is constructed from one of the original concepts, by restricting cases to the concept.

A class "MissingValue". See MV.missingValueCompletion.

Lala Septem Riza

J. Grzymala-Busse and W. Grzymala-Busse, "Handling Missing Attribute Values," in Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach, Eds. New York : Springer, 2010, pp. 33-51

MV.missingValueCompletion

#############################################
## Example: Concept Closest Fit
#############################################
dt.ex1 <- data.frame(
     c(100.2, 102.6, NA, 99.6, 99.8, 96.4, 96.6, NA), 
     c(NA, "yes", "no", "yes", NA, "yes", "no", "yes"), 
     c("no", "yes", "no", "yes", "yes", "no", "yes", NA),
     c("yes", "yes", "no", "yes", "no", "no", "no", "yes"))
colnames(dt.ex1) <- c("Temp", "Headache", "Nausea", "Flu")
decision.table <- SF.asDecisionTable(dataset = dt.ex1, decision.attr = 4, 
                                    indx.nominal = c(2:4))
indx = MV.conceptClosestFit(decision.table)