MV.mostCommonValResConcept: The most common value or mean of an attribute restricted to a...

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

Description

It is used for handling missing values by assigning the most common value of an attribute restricted to a concept. If an attributes containing missing values is continuous/real, the method uses mean of the attribute instead of the most common value. In order to generate a new decision table, we need to execute SF.applyDecTable.

Usage

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Arguments

decision.table

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

Value

A class "MissingValue". See MV.missingValueCompletion.

Author(s)

Lala Septem Riza

References

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

See Also

MV.missingValueCompletion

Examples

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#############################################
## Example: The most common value
#############################################
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.mostCommonValResConcept(decision.table)


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