# MV.mostCommonVal: Replacing missing attribute values by the attribute mean or... In RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

## Description

It is used for handling missing values by replacing the attribute mean or common values. 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

 `1` ```MV.mostCommonVal(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`.

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

`MV.missingValueCompletion`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```############################################# ## Example: Replacing missing attribute values ## by the attribute mean/common values ############################################# 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.mostCommonVal(decision.table) ```