The DBSCAN method

Description

The process shows, using the Compound dataset, the advantages of using density based clustering by using the DBSCAN clustering algorithm.

Input

Compound [SIPU Datasets] [Compound]

The dataset consist of 399 two-dimensional vectors belonging to six groups. The groups differ in the density of their points, and each set can encompass another point set as well.

Figure 11.8. The groups with varying density

The groups with varying density

Output

Figure 11.9. The results of the method with default parameters

The results of the method with default parameters


The process yields remarkable results even with default settings, only one out of the six clusters contains an error. Using the parameters epsilon and min points, results can be refined further, but as the points in the above-mentioned cluster are of different densities, a perfect solution cannot be reached.

Interpretation of the results

The operation of the DBSCAN algorithm has been demonstration on a dataset consisting of groups with different densities. The deficiencies of the algorithm have been shows, i.e. that if points of different densities can be found in a cluster, the precise operation of the algorithm cannot be guaranteed.

Video

Workflow

clust_exp3.rmp

Keywords

DBSCAN method
density function
cluster analysis

Operators

DBSCAN
Read CSV