reachability  R Documentation 
Reachability distances can be plotted to show the hierarchical relationships between data points. The idea was originally introduced by Ankerst et al (1999) for OPTICS. Later, Sanders et al (2003) showed that the visualization is useful for other hierarchical structures and introduced an algorithm to convert dendrogram representation to reachability plots.
## S3 method for class 'reachability' print(x, ...) ## S3 method for class 'reachability' plot( x, order_labels = FALSE, xlab = "Order", ylab = "Reachability dist.", main = "Reachability Plot", ... ) as.reachability(object, ...) ## S3 method for class 'dendrogram' as.reachability(object, ...)
x 
object of class 
... 
graphical parameters are passed on to 
order_labels 
whether to plot text labels for each points reachability distance. 
xlab 
xaxis label. 
ylab 
yaxis label. 
main 
Title of the plot. 
object 
any object that can be coerced to class

A reachability plot displays the points as vertical bars, were the height is the reachability distance between two consecutive points. The central idea behind reachability plots is that the ordering in which points are plotted identifies underlying hierarchical density representation as mountains and valleys of high and low reachability distance. The original ordering algorithm OPTICS as described by Ankerst et al (1999) introduced the notion of reachability plots.
OPTICS linearly orders the data points such that points which are spatially closest become neighbors in the ordering. Valleys represent clusters, which can be represented hierarchically. Although the ordering is crucial to the structure of the reachability plot, its important to note that OPTICS, like DBSCAN, is not entirely deterministic and, just like the dendrogram, isomorphisms may exist
Reachability plots were shown to essentially convey the same information as the more traditional dendrogram structure by Sanders et al (2003). An dendrograms can be converted into reachability plots.
Different hierarchical representations, such as dendrograms or reachability plots, may be preferable depending on the context. In smaller datasets, cluster memberships may be more easily identifiable through a dendrogram representation, particularly is the user is already familiar with treelike representations. For larger datasets however, a reachability plot may be preferred for visualizing macrolevel density relationships.
A variety of cluster extraction methods have been proposed using
reachability plots. Because both cluster extraction depend directly on the
ordering OPTICS produces, they are part of the optics()
interface.
Nonetheless, reachability plots can be created directly from other types of
linkage trees, and vice versa.
Note: The reachability distance for the first point is by definition not defined
(it has no preceeding point).
Also, the reachability distances can be undefined when a point does not have enough
neighbors in the epsilon neighborhood. We represent these undefined cases as Inf
and represent them in the plot as a dashed line.
An object of class reachability
with components:
order 
order to use for the data points in 
reachdist 
reachability distance for each data point in 
Matthew Piekenbrock
Ankerst, M., M. M. Breunig, H.P. Kriegel, J. Sander (1999). OPTICS: Ordering Points To Identify the Clustering Structure. ACM SIGMOD international conference on Management of data. ACM Press. pp. 49–60.
Sander, J., X. Qin, Z. Lu, N. Niu, and A. Kovarsky (2003). Automatic extraction of clusters from hierarchical clustering representations. PacificAsia Conference on Knowledge Discovery and Data Mining. Springer Berlin Heidelberg.
optics()
, as.dendrogram()
, and stats::hclust()
.
set.seed(2) n < 20 x < cbind( x = runif(4, 0, 1) + rnorm(n, sd = 0.1), y = runif(4, 0, 1) + rnorm(n, sd = 0.1) ) plot(x, xlim = range(x), ylim = c(min(x)  sd(x), max(x) + sd(x)), pch = 20) text(x = x, labels = 1:nrow(x), pos = 3) ### run OPTICS res < optics(x, eps = 10, minPts = 2) res ### plot produces a reachability plot. plot(res) ### Manually extract reachability components from OPTICS reach < as.reachability(res) reach ### plot still produces a reachability plot; points ids ### (rows in the original data) can be displayed with order_labels = TRUE plot(reach, order_labels = TRUE) ### Reachability objects can be directly converted to dendrograms dend < as.dendrogram(reach) dend plot(dend) ### A dendrogram can be converted back into a reachability object plot(as.reachability(dend))
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