The process shows, using the Flame dataset, how the ideal parameters can be found automatically.
The dataset consists of 240 two-dimensional vectors, that belong to two clusters. The clusters are aligned close to each other, and one of the clusters has a non-spherical shape.
To perform parameter optimization, a performance operator is required, which, in this case, will be the node measuring cluster distance.
The parameters to be optimized, and their possible values are chosen in the parameter optimization operator, and then, it is confided to the system to choose the ideal values.
In the present case, the best result was yielded by partitioning the task into 10 clusters, and defining the distance between them with the Euclidean distance.
For many parameterized clustering methods, it can be ideal to confide the determination of the appropriate number of clusters to a performance measurement operator, and then run the clustering with the obtained values.