R: K medoids clustering for a time series database using the...

KMedoids {TSdist} | R Documentation

## K medoids clustering for a time series database using the selected distance measure.

### Description

Given a specific distance measure and a time series database, this function provides the K-medoids clustering result. Furthermore, if the ground truth clustering is provided, and the associated F-value is also provided.

### Usage

    KMedoids(data,  k, ground.truth, distance, ...)

### Arguments

`data` | Time series database saved in a numeric matrix, a list, an `mts` object, a `zoo` object or `xts` object.
`k` | Integer value which represents the number of clusters.
`ground.truth` | Numerical vector which indicates the ground truth clustering of the database.
`distance` | Distance measure to be used. It must be one of: `"euclidean"`, `"manhattan"`, `"minkowski"`, `"infnorm"`, `"ccor"`, `"sts"`, `"dtw"`, `"keogh_lb"`, `"edr"`, `"erp"`, `"lcss"`, `"fourier"`, `"tquest"`, `"dissimfull"`, `"dissimapprox"`, `"acf"`, `"pacf"`, `"ar.lpc.ceps"`, `"ar.mah"`, `"ar.mah.statistic"`, `"ar.mah.pvalue"`, `"ar.pic"`, `"cdm"`, `"cid"`, `"cor"`, `"cort"`, `"wav"`, `"int.per"`, `"per"`, `"mindist.sax"`, `"ncd"`, `"pred"`, `"spec.glk"`, `"spec.isd"`, `"spec.llr"`, `"pdc"`, `"frechet"`)
`...` | Additional parameters required by the chosen distance measure.

### Details

This function is useful to evaluate the performance of different distance measures in the task of clustering time series.

### Value

`clustering` | Numerical vector providing the clustering result for the database.
`F` | F-value corresponding to the clustering result.

### Author(s)

Usue Mori, Alexander Mendiburu, Jose A. Lozano.

### See Also

To calculate the distance matrices of time series databases the `TSDatabaseDistances` is used.

### Examples

    # The example.database3 synthetic database is loaded
    data(example.database3)
    tsdata <- example.database3[[1]]
    groundt <- example.database3[[2]]

    # Apply K-medoids clusterning for different distance measures

    KMedoids(data=tsdata, ground.truth=groundt, k=5, "euclidean")
    KMedoids(data=tsdata, ground.truth=groundt, k=5, "cid")
    KMedoids(data=tsdata, ground.truth=groundt, k=5, "pdc")

[Package TSdist version 3.6 Index]