  Benchmark data sets are of vital importance in machine learning research, as
indicated by the number of repositories that exist to make them publicly
available. Although many of these are usable in the stream mining context as
well, it is less obvious which data sets can be used to evaluate data stream
clustering algorithms. We note that the classic Covertype data set's size makes
it attractive for use in stream mining but unfortunately it is specifically
designed for classification. Here we detail the process of transforming the
Covertype data set into one amenable for unsupervised learning, which we call
the Wilderness Area data set. Our quantitative analysis allows us to conclude
that the Wilderness Area data set is more appropriate for unsupervised learning
than the original Covertype data set.
