clusterDBSCAN.plot

Plot clusters

Since R2021a

Syntax

fh = plot(clusterer,X,idx)

fh = plot(___,'Parent',ax)

fh = plot(___,'Title',titlestr)

Description

fh = plot(clusterer,X,idx) displays a plot of DBSCAN clustering results and returns a figure handle, fh. Inputs are the cluster object, clusterer, the input data matrix, X, and cluster indices, idx.

fh = plot(___,'Parent',ax) also specifies the axes, ax, of the cluster results plot.

fh = plot(___,'Title',titlestr) also specifies the title, titlestr, of the cluster results plot.

Examples

Cluster Detections in Range and Doppler

Create detections of extended objects with measurements in range and Doppler. Assume the maximum unambiguous range is 20 m and the unambiguous Doppler span extends from $-30$ Hz to $30$ Hz. Data for this example is contained in the dataClusterDBSCAN.mat file. The first column of the data matrix represents range, and the second column represents Doppler.

The input data contains the following extended targets and false alarms:

• an unambiguous target located at $\left(10,15\right)$

• an ambiguous target in Doppler located at$\left(10,-30\right)$

• an ambiguous target in range located at $\left(20,15\right)$

• an ambiguous target in range and Doppler located at $\left(20,30\right)$

• 5 false alarms

Create a clusterDBSCAN object and specify that disambiguation is not performed by setting EnableDisambiguation to false. Solve for the cluster indices.

load('dataClusterDBSCAN.mat');
cluster1 = clusterDBSCAN('MinNumPoints',3,'Epsilon',2, ...
    'EnableDisambiguation',false);
idx = cluster1(x);

Use the clusterDBSCAN plot object function to display the clusters.

plot(cluster1,x,idx)

The plot indicates that there are eight apparent clusters and six noise points. The 'Dimension 1' label corresponds to range and the 'Dimension 2' label corresponds to Doppler.

Next, create another clusterDBSCAN object and set EnableDisambiguation to true to specify that clustering is performed across the range and Doppler ambiguity boundaries.

cluster2 = clusterDBSCAN('MinNumPoints',3,'Epsilon',2, ...
    'EnableDisambiguation',true,'AmbiguousDimension',[1 2]);

Perform the clustering using ambiguity limits and then plot the clustering results. The DBSCAN clustering results correctly show four clusters and five noise points. For example, the points at ranges close to zero are clustered with points near 20 m because the maximum unambiguous range is 20 m.

amblims = [0 maxRange; minDoppler maxDoppler];
idx = cluster2(x,amblims);
plot(cluster2,x,idx)

Input Arguments

clusterer — Clusterer object

clusterDBSCAN object

Clusterer object, specified as a clusterDBSCAN object.

X — Input data to cluster

real-valued N-by-P matrix

Input data, specified as a real-valued N-by-P matrix. The N rows correspond to points in a P-dimensional feature space. The P columns contain the values of the features over which clustering takes place. For example, a two-column input can contain Cartesian coordinates x and y, or range and Doppler.

Data Types: double

idx — Cluster indices

N-by-1 integer-valued column vector

Cluster indices, specified as an N-by-1 integer-valued column vector. Cluster indices represent the clustering results of the DBSCAN algorithm contained in the first output argument of clusterDBSCAN. idx values start at one and are consecutively numbered. The plot object function labels each cluster with the cluster index. A value of –1 in idx indicates a DBSCAN noise point. Noise points are not labeled.

Data Types: double

ax — Axes of plot

Axes handle

Axes of plot, specified as an Axes object handle.

Data Types: double

titlestr — Plot title

character vector | string

Plot title, specified as a character vector or string.

Example: 'Range-Doppler Clusters'

Data Types: char | string

Output Arguments

fh — Figure handle of plot

positive scalar

Figure handle of plot, returned as a positive scalar.

Version History

Introduced in R2021a