Abstract Machine learning has proven to be a powerful tool for knowledge extraction from
large data sets across different domains. Data quality and results interpretability are …

Clustering, an unsupervised machine learning approach, aims to find groups of similar
instances. Mixed data clustering is of particular interest since real-life data often consists of …

Technological progress has brought artificial intelligence closer to people, assuming an
important role in many fields thanks to its support. Artificial Intelligence, AI, is a technology …

Explainable Artificial Intelligence (XAI) aims to introduce transparency and intelligibility into
the decision-making process of AI systems. Most often, its application concentrates on …

Explainable Artificial Intelligence (XAI) aims at introducing transparency and intelligibility
into decision-making process of AI systems. Most of the work in this area is focused on …

Supervised machine learning explainability has developed rapidly in recent years. However,
clustering explainability has lagged behind. Here, we demonstrate the first adaptation of …

Cluster assignments, in particular the deep clustering ones, are often hard to explain,
partially because they depend on all the features of the data in a complicated way, so it is …

Contemporary machine learning techniques are capable of extracting complex structure
from data in a way that complements or exceeds manual examination, yet, as is …

A recent trend in machine learning has been to enrich learned models with the ability to
explain their own predictions. The emerging field of explainable AI (XAI) has so far mainly …

The domain of explainable AI is of interest in all Machine Learning fields, and it is all the
more important in clustering, an unsupervised task whose result must be validated by a …