=Paper=
{{Paper
|id=Vol-2436/invited_1
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2436/invited_1.pdf
|volume=Vol-2436
|dblpUrl=https://dblp.org/rec/conf/sdm/Campello19
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2436/invited_1.pdf</pdf>
<pre>
                              Evaluation of Unsupervised Learning Results:
                               Making the Seemingly Impossible Possible

                                          Ricardo J. G. B. Campello∗
                                                   May 4, 2019



Abstract of Invited Presentation
When labels are not around, evaluating the final or intermediate results produced by a learning algorithm is
usually not simple. In cluster analysis and unsupervised outlier detection, evaluation is important in many
different aspects. It is a crucial task, e.g., for model selection, model validation, assessment of ensemble members
accuracy and diversity, among others. In cluster analysis this task has been investigated for decades, but it is
relatively well understood only under certain oversimplified model assumptions. In outlier detection, unsupervised
evaluation is still in its infancy. Even when labels are available in the form of a ground-truth, such as in controlled
benchmarking experiments, evaluation can still be challenging because the semantic described by the ground-truth
labels may not be properly captured by the data as represented in the given feature space.
     In this talk, I intend to discuss some particular aspects regarding outlier and clustering evaluation, focusing
on a few recent results as well as some challenges for future research.




  ∗ University of Newcastle

</pre>