# MV.globalClosestFit: Global Closest Fit In RoughSets: Data Analysis Using Rough Set and Fuzzy Rough Set Theories

## Description

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

## Usage

 `1` ```MV.globalClosestFit(decision.table) ```

## Arguments

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

## Details

The global closes fit method is based on replacing a missing attribute value by the known value in another case that resembles as much as possible the case with the missing attribute value. In searching for the closest fit case we compare two vectors of attribute values, one vector corresponds to the case with a missing attribute value, the other vector is a candidate for the closest fit. The search is conducted for all cases, hence the name global closest fit. For each case a distance is computed, the case for which the distance is the smallest is the closest fitting case that is used to determine the missing attribute value.

## 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``` ```############################################# ## Example: Global 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.globalClosestFit(decision.table) ```