R: ~ Function: parKml ~

parKml {kml} | R Documentation

## ~ Function: parKml ~

### Description

`parKml` and `parALGO` are constructor for the object `ParKml`.

### Usage

    parKml(saveFreq,maxIt,imputationMethod,distanceName,power,distance,
       centerMethod,startingCond,nbCriterion,scale)

    parALGO(saveFreq=100,maxIt=200,imputationMethod="copyMean",
       distanceName="euclidean",power=2,distance=function(){},
       centerMethod=meanNA,startingCond="nearlyAll",nbCriterion=1000,scale=TRUE)

### Arguments

`saveFreq` | `[numeric]`: Long computations can take several days. So it is possible to save the object `ClusterLongData` on which works `kml` once in a while. `saveFreq` defines the frequency of the saving process. The `ClusterLongData` is saved every `saveFreq` clustering calculations. The object is saved in the file `objectName.Rdata` in the curent folder. If `saveFreq` is set to `Inf`, the object is never saved.
`maxIt` | `[numeric]`: Set a limit to the number of iteration if convergence is not reached.
`imputationMethod` | `[character]`: the calculation of quality criterion can not be done if some value are missing. `imputationMethod` define the method use to impute the missing value. See `imputation` for detail.
`distanceName` | `[character]`: name of the `distance` used by k-means. If the `distanceName` is one of "manhattan", "euclidean", "minkowski", "maximum", "canberra" or "binary", a compiled optimized version specificaly design for trajectories version is used. Otherwise, the function define in the slot `distance` is used.
`power` | `[numeric]`: If `distanceName="minkowski"`, this define the power that will be used.
`distance` | `[numeric <- function(trajA,trajB)]`: function that computes the distance between two trajectories. If no function is specified, the Euclidian distance with Gower adjustment (to deal with missing value) is used.
`centerMethod` | `[numeric <- function(vector(numeric))]`: k-means algorithm computes the centers of each cluster. It is possible to personalize the definition of "center" by defining a function "centerMethod". This function should take a vector of numeric as argument and return a single numeric -the center of the vector-.
`startingCond` | `[character]`: specifies the starting condition. Should be one of "randomAll", "randomK", "maxDist", "kmeans++", "kmeans+", "kmeans-" or "kmeans–" (see `initializePartition` for details). It also could take two specifics values: "all" stands for c("maxDist","kmeans-") then an alternance of "kmeans–" and "randomK" while "nearlyAll" stands for "kmeans-" then an alternance of "kmeans–" and "randomK".

`nbCriterion` | `[numeric]`: set the maximum number of quality criterion that are display on the graph (since displaying a high criterion number an slow down the overall process). The default value is 100.
`scale` | `[logical]`: if TRUE, then the data will be automaticaly scaled (using the function `scale` with default values) before the execution of k-means on joint trajectories. Then the data will be restore (using the function `restoreRealData`) just before the end of the function `kml3d`. This option has no effect on `kml`.

### Details

`parKml` is the constructor of object `ParKml`.

### Value

An object `ParKml`.

### Examples

    ### Generation of some data
    cld1 <- generateArtificialLongData()

    ### Setting two different set of option :
    (option1 <- parALGO())
    (option2 <- parALGO(distanceName="maximum",centerMethod=function(x)median(x,na.rm=TRUE)))

    ### Running kml We suspect 3, 4 or 5 clusters, we want 3 redrawing.
    kml(cld1,3:5,3,toPlot="both",parAlgo=option1)
    kml(cld1,3:5,3,toPlot="both",parAlgo=option2)

[Package kml version 2.4.1 Index]