Abstract
AutoML has witnessed effective applications in the field of supervised learning – mainly in classification tasks – where the goal is to find the best machine-learning pipeline when a ground truth is available. This is not the case for unsupervised tasks that are by nature exploratory and they are performed to unveil hidden insights. Since there is no right result, analyzing different configurations is more important than returning the best-performing one. When it comes to exploratory unsupervised tasks – such as cluster analysis – different facets of the datasets could be interesting for the data scientist; for instance, data items can be effectively grouped together in different subspaces of features. In this paper, AutoClues explores and returns a dashboard of both relevant and diverse clusterings via AutoML and diversification. AutoML ensures that the explored pipelines for cluster analysis (including pre-processing steps) compute good clusterings. Then, diversification selects, out of the explored clusterings, the ones conveying different clues to the data scientists.
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Notes
- 1.
This dimensionality reduction visualizes high-dimensional clusterings in 2D, preserving distance proportions. We apply it with the default Scikit-learn hyperparameters.
- 2.
If an algorithm has no hyperparameters (\(\varLambda _{A} = \varnothing \)), we set a placeholder \(\varLambda _{A} = \{ 1 \}\).
- 3.
- 4.
In statistics, it serves as a baseline for assessing the significance in random variations.
- 5.
We use the default hyperparameter \(\beta = 0.5\), and set \(\alpha \) according to the test at hand.
- 6.
Metrics are computed on the original dataset (i.e., no t-SNE distortion).
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Francia, M., Giovanelli, J., Golfarelli, M. (2024). AutoClues: Exploring Clustering Pipelines via AutoML and Diversification. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14645. Springer, Singapore. https://doi.org/10.1007/978-981-97-2242-6_20
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