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The easiest way is to place the clustering operator together with a number of "daisy-chained" cluster performance operators within an Optimize Parameters (Grid). The optimizer will allow you to vary DBSCAN parameters, such as epsilon and the minimum points, while performance is collected and logged for each combination of these parameters. At the end (and while it is executing) you can watch and plot the logged values. Note that DBSCAN is slow on very large data sets, so I suggest to take a smaller data sample and initially vary the parameters in large steps. Once you find the best "cube" of your parameters, tune the parameters with the finer comb.
Sort by:
1 - 1 of
11
The easiest way is to place the clustering operator together with a number of "daisy-chained" cluster performance operators within an Optimize Parameters (Grid). The optimizer will allow you to vary DBSCAN parameters, such as epsilon and the minimum points, while performance is collected and logged for each combination of these parameters. At the end (and while it is executing) you can watch and plot the logged values. Note that DBSCAN is slow on very large data sets, so I suggest to take a smaller data sample and initially vary the parameters in large steps. Once you find the best "cube" of your parameters, tune the parameters with the finer comb.