Computer Science > Machine Learning
arXiv:2307.08104 (cs)
[Submitted on 16 Jul 2023]
Title:Using Decision Trees for Interpretable Supervised Clustering
View a PDF of the paper titled Using Decision Trees for Interpretable Supervised Clustering, by Natallia Kokash and Leonid Makhnist
View PDFAbstract:In this paper, we address an issue of finding explainable clusters of class-uniform data in labelled datasets. The issue falls into the domain of interpretable supervised clustering. Unlike traditional clustering, supervised clustering aims at forming clusters of labelled data with high probability densities. We are particularly interested in finding clusters of data of a given class and describing the clusters with the set of comprehensive rules. We propose an iterative method to extract high-density clusters with the help of decisiontree-based classifiers as the most intuitive learning method, and discuss the method of node selection to maximize quality of identified groups.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2307.08104 [cs.LG] |
| (orarXiv:2307.08104v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2307.08104 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Using Decision Trees for Interpretable Supervised Clustering, by Natallia Kokash and Leonid Makhnist
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