Explainable K-Means Clustering for Occupancy Estimation

Abstract

Explainable AI (Artificial Intelligence) has gained significant popularity in recent years. It provides reliable and helpful insights about the model and its output predictions. Even though Explainable AI is an important topic thanks to its interpretability, there have been only very few works that are conducted under the unsupervised learning task. This research aims to apply an interpretable unsupervised machine learning method to the task of Occupancy Estimation (OE). Estimating the number of occupants in buildings has been proven to be helpful to several applications such as energy management. Explainable AI increases the credibility of the applications based on OE by giving the user an interpretation of the obtained OE predictions using simple If-Then rules. In all previous research works on clustering methods either results were so difficult to interpret or data were hard to label. To avert these problems and enhance the understanding of the clustering decision, we propose a small binary decision tree to divide the dataset into k clusters so that the interpretability of the obtained k clusters will be simple to understand. It is known that with an increase in the number of leaves of the decision tree, the accuracy of the clusters increases, the cost of clustering increases, and the interpretability of the obtained clusters will be more difficult to deal with. In this work, we applied and tested an unsupervised machine learning model known as the explainable K-means clustering (ExKMC) algorithm for occupancy estimation. ExKMC by default creates a small tree with k leaves that partitions the data into k clusters, and it also outputs a new tree with k' leaves where k' ≥k that provides explainable clusters. The considered method makes a simple trade-off between the accuracy of prediction and the interpretability of the clustering decisions.