Clustering Results Repository (v1.2.0)

We have prepared a repository of clustering results for problems from our Benchmark Suite (v1.2.0).

A non-interactive results catalogue is available here.

Feature Engineering

The algorithms operated on the original data spaces, i.e., subject to only some mild preprocessing:

• columns of zero variance have been removed;

• a tiny amount of white noise has been added to each datum to make sure the distance matrices consist of unique elements (this guarantees that the results of hierarchical clustering algorithms are unambiguous);

• all data were translated and scaled proportionally to assure that each column is of mean 0 and that the total variance of all entries is 1 (this is not standardisation).

Note, however, that spectral clustering and Gaussian mixtures can be considered as ones that modify the input data space.

Overall, comparisons between distance-based methods that apply automated feature engineering/selection and those that only operate on raw inputs are not exactly fair. In such settings, the classical methods should be run on the transformed data spaces as well. This is left for further research (stay tuned).