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

MV.mostCommonValResConcept {RoughSets} | R Documentation

## The most common value or mean of an attribute restricted to a concept

### 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

    MV.mostCommonValResConcept(decision.table)

### 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

    #############################################
    ## 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)

[Package RoughSets version 1.3-7 Index]