Dbscan

Purpose

Density-based automatic sample clustering.

Synopsis

[cls,eps] = dbscan(data,minpts,eps)

Description

DBSCAN automatically identifies clusters in data (or scores) using a density-based algorithm. Samples which are within an "acceptable" distance are agglomerated into a single class. Samples which are too far from any cluster and do not have a minimum number of un-assigned neighbors are assigned as "noise" (although such points may be re-assigned as a class if a class is identified acceptably close by).

Inputs

• data = A double or dataset object.

Optional Inputs

• minpts = The minimum number of unclassed points which should be considered a "class" (default = 2)
• eps = The largest distance between samples considered to be related (can also be considered the minimum distance between unrelated classes.) Default: determined by the range and number of data points available.

Outputs

• cls = Numerical classes for each of the m samples in the original data. Samples excluded in original dataset are always returned as class 0 (zero, unknown.)
• eps = The eps value used (useful if no eps value was supplied by user.)

See Also

cluster, knn, pca