R: Nearest neighbor based clutter/noise detection

NNclean {prabclus} | R Documentation

## Nearest neighbor based clutter/noise detection

### Description

Detects if data points are noise or part of a cluster, based on a Poisson process model.

### Usage

    NNclean(data, k, distances = NULL, edge.correct = FALSE, wrap = 0.1,
    convergence = 0.001, plot=FALSE, quiet=TRUE)

    ## S3 method for class 'nnclean'
    print(x, ...)

### Arguments

`data` | numerical matrix or data frame.
`k` | integer. Number of considered nearest neighbors per point.
`distances` | distance matrix object of class `dist`. If specified, it is used instead of computing distances from the data.
`edge.correct` | logical. If `TRUE` and the data is two-dimensional, neighbors for points at the edges of the parent region of the noise Poisson process are determined after wrapping the region onto a toroid.
`wrap` | numerical. If `edge.correct=TRUE`, points in a strip of size `wrap*range` along the edge for each variable are candidates for being neighbors of points from the opposite.
`convergence` | numerical. Convergence criterion for EM-algorithm.
`plot` | logical. If `TRUE`, a histogram of the distance to kth nearest neighbor and fit is plotted.
`quiet` | logical. If `FALSE`, the likelihood is printed during the iterations.
`x` | object of class `nnclean`.
`...` | necessary for print methods.

### Details

The assumption is that the noise is distributed as a homogeneous Poisson process on a certain region and the clusters are distributed as a homogeneous Poisson process with larger intensity on a subregion (disconnected in case of more than one cluster). The distances are then distributed according to a mixture of two transformed Gamma distributions, and this mixture is estimated via the EM-algorithm. The points are assigned to noise or cluster component by use of the estimated a posteriori probabilities.

### Value

`NNclean` returns a list of class `nnclean` with components

`z` | 0-1-vector of length of the number of data points. 1 means cluster, 0 means noise.
`probs` | vector of estimated a priori probabilities for each point to belong to the cluster component.
`k` | see above.
`lambda1` | intensity parameter of cluster component.
`lambda2` | intensity parameter of noise component.
`p` | estimated probability of cluster component.
`kthNND` | distance to kth nearest neighbor.

### Note

The software can be freely used for non-commercial purposes, and can be freely distributed for non-commercial purposes only.

### Author(s)

R-port by Christian Hennig chrish@stats.ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche,
original Splus package by S. Byers and A. E. Raftery.

### References

Byers, S. and Raftery, A. E. (1998) Nearest-Neighbor Clutter Removal for Estimating Features in Spatial Point Processes, Journal of the American Statistical Association, 93, 577-584.

### Examples

    library(mclust)
    data(chevron)
    nnc <-  NNclean(chevron[,2:3],15,plot=TRUE)
    plot(chevron[,2:3],col=1+nnc$z)

[Package prabclus version 2.2-6 Index]