R: Data Sharpening Based on K-nearest Neighbors

shrinking {clues} | R Documentation

## Data Sharpening Based on K-nearest Neighbors

### Description

Data sharpening based on K-nearest neighbors.

### Usage

    shrinking(y, K, disMethod = "Euclidean", eps = 1e-04, itmax = 20)

### Arguments

`y` | data matrix with rows being the observations and columns being variables.
`K` | number of nearest neighbors.
`disMethod` | specification of the dissimilarity measure. The available measures are “Euclidean” and “1-corr”.
`eps` | a small positive number. A value is regarded as zero if it is less than `eps`.
`itmax` | maximum number of iterations allowed.

### Details

Within each iteration, each data point is replaced by the vector of the coordinate-wise medians of its `K` nearest neighbors. Data points will move toward the locally most dense data point by this shrinking process.

### Value

Sharpened data set.

### Examples

        # Maronna data set
        data(Maronna)
        # data matrix
        maronna <- Maronna$maronna

        # cluster membership
        maronna.mem <- Maronna$maronna.mem

        tt <- shrinking(maronna, K = 5, itmax=1)
        plotClusters(tt, maronna.mem)

[Package clues version 0.6.2.2 Index]