geva.dcluster {geva} | R Documentation

## GEVA Density Clustering

### Description

Performs a density cluster analysis from summarized data.

### Usage

    geva.dcluster(
      sv,
      resolution = 0.3,
      dcluster.method = options.dcluster.method,
      cl.score.method = options.cl.score.method,
      minpts = 2,
      ...,
      eps = NA_real_,
      include.raw.results = FALSE
    )

    options.dcluster.method
    # c("dbscan", "optics")

### Arguments

`sv` | a `numeric` `SVTable` object (usually `GEVASummary`)
`resolution` | `numeric` (`0` to `1`), used as a "zoom" parameter for cluster detection. A zero value returns the minimum number of clusters that can detected, while `1` returns the maximum amount of detectable clusters. Ignored if `eps` is specified
`dcluster.method` | `character`, density-based method for cluster separation
`cl.score.method` | `character`, method used to calculate the cluster scores for each point. If `"auto"`, the `"density"` method is selected
`minpts` | `integer`, minimum number of points required to form a cluster
`...` | additional arguments. Accepts `verbose` (`logical`, default is `TRUE`) to enable or disable printing the current progress
`eps` | `numeric`, maximum neighborhood distance between points to be clustered
`include.raw.results` | `logical`, whether to attach intermediate results to the returned object

### Details

This function performs a density cluster analysis with the aid of implemented methods from the `dbscan::dbscan` package. The available methods for the `dcluster.method` arguments are `"dbscan"` and `"options"`, which internally call `dbscan::dbscan()` and `dbscan::optics()`, respectively.

The `resolution` value is an accessible way to define the cluster separation threshold used in density clustering. The DBSCAN algorithm uses an epsilon value that represents the minimum distance of separation, and `resolution` translates a value between `0` and `1` to a propotional value within the acceptable range of epsilon values. This allows defining the rate of clusters from `0` to `1`, which results in the least number of possible clusters for `0` and the highest number for `1`. Nevertheless, if epsilon is specified as `eps` in the optinal arguments, its value is used and `resolution` is ignored.

The `cl.score.method` argument defines how scores are calculated for each SV point (row in `sv`) that was assigned to a cluster, (i.e., excluding non-clustered points). If specified as `"auto"`, the parameter will be selected based on the rate of neighbor points (`"density"`).

If `include.raw.results` is `TRUE`, some aditional data will be attached to the `info` slot of the returned `GEVACluster` objects, including the kNN tree generated during the intermediate steps.

### Value

A `GEVACluster` object

### Note

In density clustering, only the most dense points are clustered. For the unclustered points, the grouping value is set to `NA`.

### See Also

Other geva.cluster: `geva.cluster()`, `geva.hcluster()`, `geva.quantiles()`

### Examples

    ## Density clustering from a randomly generated input

    # Preparing the data
    ginput <- geva.ideal.example()      # Generates a random input example
    gsummary <- geva.summarize(ginput)  # Summarizes with the default parameters

    # Density clustering
    gclust <- geva.dcluster(gsummary)
    plot(gclust)

    # Density clustering with slightly more resolution
    gclust <- geva.dcluster(gsummary, resolution=0.35)
    plot(gclust)

[Package geva version 1.0.0 Index]