R: Data Clustering (After Data Shrinking)

clustering {clues} | R Documentation

## Data Clustering (After Data Shrinking)

### Description

Data clustering (after data shrinking).

### Usage

    clustering(y, disMethod = "Euclidean")

### Arguments

`y` | data matrix which is an R matrix object (for dimension > 1) or vector object (for dimension=1) with rows be observations and columns be variables.
`disMethod` | specification of the dissimilarity measure. The available measures are “Euclidean” and “1-corr”.

### Details

We first store the first observation (data point) in `point[1]`. We then get the nearest neighbor of `point[1]`. Store it in `point[2]`. Store the dissimilarity between `point[1]` and `point[2]` to `db[1]`. We next remove `point[1]`. We then find the nearest neighbor of `point[2]`. Store it in `point[3]`. Store the dissimilarity between `point[2]` and `point[3]` to `db[2]`. We then remove `point[2]` and find the nearest neighbor of `point[3]`. We repeat this procudure until we find `point[n]` and `db[n-1]` where `n` is the total number of data points.

Next, we calculate the interquartile range (IQR) of the vector `db`. We then check which elements of `db` are larger than `avg+1.5IQR` where `avg` is the average of the vector `db`. The mininum value of these outlier dissimilarities will be stored in `omin`. An estimate of the number of clusters is `g` where `g-1` is the number of the outlier dissimilarities. The position of an outlier dissimilarity indicates the end of a cluster and the start of a new cluster.

To get a reasonable clustering result, data sharpening (shrinking) is recommended before data clustering.

### Value

`mem` | vector of the cluster membership of data points. The cluster membership takes values: 1, 2, …, g, where g is the estimated number of clusters.
`size` | vector of the number of data points for clusters.
`g` | an estimate of the number of clusters.
`db` | vector of dissimilarities between sorted consecutive data points (c.f. details).
`point` | vector of sorted consecutive data points (c.f. details).
`omin` | The minimum value of the outlier dissimilarities (c.f. details).

### References

Wang, S., Qiu, W., and Zamar, R. H. (2007). CLUES: A non-parametric clustering method based on local shrinking. Computational Statistics & Data Analysis, Vol. 52, issue 1, pages 286-298.

### Examples

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

        tt <- shrinking(maronna, K = 50, itmax = 20)
        tt2 <- clustering(tt)

        # Plot of disimilarities between the sorted consecutive data points
        #     versus the sorted consecutive data points
        # This plot can be used to assess the estimated number of clusters
        db <- tt2$db
        point <- tt2$point
        n <- length(point)
        plot(1:(n - 1), db, type = "l",
            xlab = "sorted consecutive data points",
            ylab = "disimilarities between the sorted consecutive data points",
            xlim = c(0, n), axes = FALSE)
        box()
        axis(side = 2)
        axis(side = 1, at = c(0, 1:(n - 1)), labels = point)

[Package clues version 0.6.2.2 Index]