An Explainable Artificial Intelligence Model for Clustering Numerical Databases

Abstract

Nowadays, the international scientific community of machine learning has an enormous campaign in favor of creating understandable models instead of black-box models. The main reason is that experts in the application area are showing reluctance due to black-box models cannot be understood by them, and consequently, their results are difficult to be explained. In unsupervised problems, where experts have not labeled objects, obtaining an explanation of the results is necessary because specialists in the application area need to understand both the applied model as well as the obtained results for finding the rationale behind each obtained clustering from a practical point of view. Hence, in this paper, we introduce a clustering based on decision trees (eUD3.5), which builds several decision trees from numerical databases. Unlike previous solutions, our proposal takes into account both separation and compactness for evaluating a feature split without decreasing time efficiency and with no empirical parameter to control the depth of the trees. We tested eUD3.5 on 40 numerical databases of UCI Machine Learning Repository, showing that our proposal builds a set of high-quality unsupervised decision trees for clustering, allowing us to obtain the best average ranking compared with other popular state-of-the-art clustering solutions. Also, from the collection of unsupervised decision trees induced by our proposal, a set of high-quality patterns are extracted for showing the main feature-value pairs describing each cluster. INDEX TERMS Explainable model, clustering, unsupervised decision trees, numerical databases.

Explainable AI Framework for Multivariate Hydrochemical Time Series

“…11) or spherical cluster structure were inappropriate (SI D, Fig.10, SI B, Table 3). It follows that most conventional approaches for clustering listed in [34] or recent XAIs [27,28] would be not appropriate to detect meaningful and relevant data structures. Statistical testing indicates that the distributions of interesting variables differ between classes (SI C and E).…”

“…If the algorithm produces more leaf nodes than specified by the user, then leaf nodes are combined using k-means. The authors claim to have similar performance to k-means and better performance than other conventional decision tree clustering algorithms [27].…”

“…Typical interpretable ML systems or so-called XAIs comprise combinations of neural networks with rule-based expert systems [18,19], Bayesian networks with rule mining [20], hybrids of clustering and fuzzy classification [21] or neuro-fuzzy classification [22], interpretable decision sets [23] or decision tables [24], decision tree clustering [25] or clustering combined with generative models [26]. Two of the most recent XAI approaches are the unsupervised decision tree clustering eUD3.5 [27] and a hybrid of k-means clustering and a top-down decision tree [28].…”

“…Loyola-González et al present the eUD3.5 algorithm, which uses a split criterion based on the silhouette index [27]. The silhouette index compares every object of a cluster to its homogeneity within a cluster with the heterogeneity to other clusters [29].…”

“…Applying the eUD3.5 algorithm [27] to the unprocessed data identified three clusters and resulted in 541 rules that explain various overlaps in the data points of the three clusters. The seven outliers identified in our analysis were disregarded from the data before using eUD3.5.…”