Description Usage Arguments Value Examples

Return sorted data points using maximum within-cluster distance

1 | ```
maxWithinClust(data, estep, previousResultIndicies, distanceMethod)
``` |

`data` |
the data you wish to use |

`estep` |
output from the mclust function "estep" |

`previousResultIndicies` |
indicies of rows the imbc algorithm previously queried the user on |

`distanceMethod` |
a method used to find distances between points. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski", with a default of "euclidean." |

a vector of points sorted according to the maximum within-cluster distance method algorithm

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
#Load data
library(mclust)
data(banknote)
#Create new dataset with only continuous variables
bankdata <- banknote[,2:7]
#create Mclust object
object <- Mclust(bankdata)
#determine best model
model <- object$modelName
#extract model parameters
param <- object$parameters
#output from estep of EM algorithm
estepbank <- estep(model, bankdata, param)
#we assume we have not previously queried any points
#if this vector contained any row indicies, those would not show up in output sorted vector of the function
previousResultIndicies <- c()
#output from minimax algorithm: returns vector of points that are least confidently placed in their respective classes
maxWithinClust(bankdata, estep, previousResultIndicies)
#try with a different distance method
maxWithinClust(bankdata, estep, previousResultIndicies, distanceMethod = "manhattan")
``` |

lsheremet/imbc documentation built on May 20, 2019, 7:01 p.m.

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