optics: Ordering Points to Identify the Clustering Structure (OPTICS)

In dbscan: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms

Ordering Points to Identify the Clustering Structure (OPTICS)

Description

Implementation of the OPTICS (Ordering points to identify the clustering structure) point ordering algorithm using a kd-tree.

Usage

optics(x, eps = NULL, minPts = 5, ...)

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

## S3 method for class 'optics'
plot(x, cluster = TRUE, predecessor = FALSE, ...)

## S3 method for class 'optics'
as.reachability(object, ...)

## S3 method for class 'optics'
as.dendrogram(object, ...)

extractDBSCAN(object, eps_cl)

extractXi(object, xi, minimum = FALSE, correctPredecessors = TRUE)

## S3 method for class 'optics'
predict(object, newdata, data, ...)

Arguments

x	a data matrix or a dist object.
eps	upper limit of the size of the epsilon neighborhood. Limiting the neighborhood size improves performance and has no or very little impact on the ordering as long as it is not set too low. If not specified, the largest minPts-distance in the data set is used which gives the same result as infinity.
minPts	the parameter is used to identify dense neighborhoods and the reachability distance is calculated as the distance to the minPts nearest neighbor. Controls the smoothness of the reachability distribution. Default is 5 points.
...	additional arguments are passed on to fixed-radius nearest neighbor search algorithm. See frNN() for details on how to control the search strategy.
cluster, predecessor	plot clusters and predecessors.
object	clustering object.
eps_cl	Threshold to identify clusters (eps_cl <= eps).
xi	Steepness threshold to identify clusters hierarchically using the Xi method.
minimum	logical, representing whether or not to extract the minimal (non-overlapping) clusters in the Xi clustering algorithm.
correctPredecessors	logical, correct a common artifact by pruning the steep up area for points that have predecessors not in the cluster–found by the ELKI framework, see details below.
newdata	new data points for which the cluster membership should be predicted.
data	the data set used to create the clustering object.

Details

The algorithm

This implementation of OPTICS implements the original algorithm as described by Ankerst et al (1999). OPTICS is an ordering algorithm with methods to extract a clustering from the ordering. While using similar concepts as DBSCAN, for OPTICS eps is only an upper limit for the neighborhood size used to reduce computational complexity. Note that minPts in OPTICS has a different effect then in DBSCAN. It is used to define dense neighborhoods, but since eps is typically set rather high, this does not effect the ordering much. However, it is also used to calculate the reachability distance and larger values will make the reachability distance plot smoother.

OPTICS linearly orders the data points such that points which are spatially closest become neighbors in the ordering. The closest analog to this ordering is dendrogram in single-link hierarchical clustering. The algorithm also calculates the reachability distance for each point. plot() (see reachability_plot) produces a reachability plot which shows each points reachability distance between two consecutive points where the points are sorted by OPTICS. Valleys represent clusters (the deeper the valley, the more dense the cluster) and high points indicate points between clusters.

Specifying the data

If x is specified as a data matrix, then Euclidean distances and fast nearest neighbor lookup using a kd-tree are used. See kNN() for details on the parameters for the kd-tree.

Extracting a clustering

Several methods to extract a clustering from the order returned by OPTICS are implemented:

• extractDBSCAN() extracts a clustering from an OPTICS ordering that is similar to what DBSCAN would produce with an eps set to eps_cl (see Ankerst et al, 1999). The only difference to a DBSCAN clustering is that OPTICS is not able to assign some border points and reports them instead as noise.

• extractXi() extract clusters hierarchically specified in Ankerst et al (1999) based on the steepness of the reachability plot. One interpretation of the xi parameter is that it classifies clusters by change in relative cluster density. The used algorithm was originally contributed by the ELKI framework and is explained in Schubert et al (2018), but contains a set of fixes.

Predict cluster memberships

predict() requires an extracted DBSCAN clustering with extractDBSCAN() and then uses predict for dbscan().

Value

An object of class optics with components:

eps	value of eps parameter.
minPts	value of minPts parameter.
order	optics order for the data points in x.
reachdist	reachability distance for each data point in x.
coredist	core distance for each data point in x.

For extractDBSCAN(), in addition the following components are available:

eps_cl	the value of the eps_cl parameter.
cluster	assigned cluster labels in the order of the data points in x.

For extractXi(), in addition the following components are available:

xi	Steepness thresholdx.
cluster	assigned cluster labels in the order of the data points in x.
clusters_xi	data.frame containing the start and end of each cluster found in the OPTICS ordering.

Author(s)

Michael Hahsler and Matthew Piekenbrock

References

Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Joerg Sander (1999). OPTICS: Ordering Points To Identify the Clustering Structure. ACM SIGMOD international conference on Management of data. ACM Press. pp. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1145/304181.304187")}

Hahsler M, Piekenbrock M, Doran D (2019). dbscan: Fast Density-Based Clustering with R. Journal of Statistical Software, 91(1), 1-30. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v091.i01")}

Erich Schubert, Michael Gertz (2018). Improving the Cluster Structure Extracted from OPTICS Plots. In Lernen, Wissen, Daten, Analysen (LWDA 2018), pp. 318-329.

See Also

Density reachability.

Other clustering functions: dbscan(), extractFOSC(), hdbscan(), jpclust(), sNNclust()

Examples

set.seed(2)
n <- 400

x <- cbind(
  x = runif(4, 0, 1) + rnorm(n, sd = 0.1),
  y = runif(4, 0, 1) + rnorm(n, sd = 0.1)
  )

plot(x, col=rep(1:4, time = 100))

### run OPTICS (Note: we use the default eps calculation)
res <- optics(x, minPts = 10)
res

### get order
res$order

### plot produces a reachability plot
plot(res)

### plot the order of points in the reachability plot
plot(x, col = "grey")
polygon(x[res$order, ])

### extract a DBSCAN clustering by cutting the reachability plot at eps_cl
res <- extractDBSCAN(res, eps_cl = .065)
res

plot(res)  ## black is noise
hullplot(x, res)

### re-cut at a higher eps threshold
res <- extractDBSCAN(res, eps_cl = .07)
res
plot(res)
hullplot(x, res)

### extract hierarchical clustering of varying density using the Xi method
res <- extractXi(res, xi = 0.01)
res

plot(res)
hullplot(x, res)

# Xi cluster structure
res$clusters_xi

### use OPTICS on a precomputed distance matrix
d <- dist(x)
res <- optics(d, minPts = 10)
plot(res)

dbscan documentation built on June 29, 2024, 1:07 a.m.