Accepted paper at ICDM 2020 Workshop HDM'20

Authors

Daniyal Kazempour, Peer Kröger, Thomas Seidl

The 8th ICDM Workshop on High Dimensional Data Mining (HDM'20)
in conjunction with the 20th IEEE International Conference on Data Mining (ICDM 2020),
17–20 November 2020, Virtual

Abstract

In the setting of unsupervised machine learning, especially in clustering tasks, the evaluation of either novel algorithms or the assessment of a clustering of novel data is challenging. While mostly in the literature the evaluation of new methods is performed on labelled data, there are cases where no labels are at our disposal. In other cases we may not want to trust the "ground truth" labels. In general there exists a spectrum of so called internal evaluation measures in the literature. Each of the measures is mostly specialized towards a specific clustering model. The model of arbitrarily oriented subspace clusters is a more recent one. To the best of our knowledge there exist at the current time no internal evaluation measures tailored at assessing this particular type of clusterings. In this work we present the first internal quality measures for arbitrarily oriented subspace clusterings namely the normalized projected energy (NPE) and subspace compactness score (SCS). The results from the experiments show that especially NPE is capable of assessing clusterings by considering archetypical properties of arbitrarily oriented subspace clustering.