Talks and Poster Presentations (with Proceedings-Entry):
F. Iglesias Vazquez, T. Zseby, A. Zimek:
"Interpretability and Refinement of Clustering";
Talk: IEEE International Conference on Data Science and Advanced Analytics (DSAA),
Sydney, Australia;
09-06-2020
- 09-09-2020; in: "Proceedings of the 7th DSAA 2020",
(2020),
ISBN: 978-1-7281-8206-3;
21
- 29.
English abstract:
The difficulty to validate clustering reliability hinders the adoption of clustering in real-life applications. We pro-pose: (a) a set of symbolic representations to interpret problem spaces and (b) the CluReAL algorithm to refine any clustering result regardless of the used technique. Both approaches are grounded by recently published absolute cluster validity indices.Conducted experiments show how the refinement algorithm improves performances in a wide variety of scenarios and builds more interpretable solutions, whereas symbolic representations are shown to offer explainable summaries of problem contexts.Refinement and interpretability are both crucial to reduce failure and increase performance control and operational awareness in processes that depend on clustering.
Keywords:
cluster validity, machine learning interpretability, cluster refinement
Electronic version of the publication:
https://ieeexplore.ieee.org/document/9260005
Created from the Publication Database of the Vienna University of Technology.