Explainable Clustering: A solution to interpret and describe clusters
DOI:
https://doi.org/10.5753/jidm.2025.4663Keywords:
clustering explainability, unsupervised learning, Explainable Artificial Intelligence, XAIAbstract
Unsupervised learning algorithms represent a set of techniques for finding hidden patterns or characteristics in data without a previously defined label. An unsupervised learning technique is clustering, which consists of grouping data with similar characteristics into the same group, while data with different characteristics belong to other groups. Despite being a technique with many applications, understanding the output of clustering models is a complex task, requiring extensive manual analysis to understand the characteristics of each group, since the output doesn't contain much information about the cluster's characteristics. Therefore, this article proposes MAACLI: Model and Algorithm Agnostic CLustering Interpretability, a technique for generating user-friendly descriptions to help interpret groups generated by unsupervised clustering algorithms. The solution consists of two components that generate friendly descriptions of the groups and was tested on two types of datasets, one of which was provided by a partner company. The solution was able to generate simple, user-friendly descriptions of the groups, extracting only the important attributes.
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