ExClus: Explainable Clustering on Low-dimensional Data Representations

TL;DR

ExClus is a novel method and interactive tool that automatically generates interpretable explanations for clustering results in low-dimensional projections, aiding user understanding of complex data structures.

Contribution

The paper introduces ExClus, a new approach combining information theory and greedy optimization to produce interpretable high-dimensional explanations for low-dimensional clustering.

Findings

ExClus provides informative, easy-to-understand cluster explanations.

The method balances explanation complexity and informativeness effectively.

Experiments demonstrate ExClus's efficiency and areas for scalability improvement.

Abstract

Dimensionality reduction and clustering techniques are frequently used to analyze complex data sets, but their results are often not easy to interpret. We consider how to support users in interpreting apparent cluster structure on scatter plots where the axes are not directly interpretable, such as when the data is projected onto a two-dimensional space using a dimensionality-reduction method. Specifically, we propose a new method to compute an interpretable clustering automatically, where the explanation is in the original high-dimensional space and the clustering is coherent in the low-dimensional projection. It provides a tunable balance between the complexity and the amount of information provided, through the use of information theory. We study the computational complexity of this problem and introduce restrictions on the search space of solutions to arrive at an efficient, tunable, greedy optimization algorithm. This algorithm is furthermore implemented in an interactive tool called ExClus. Experiments on several data sets highlight that ExClus can provide informative and easy-to-understand patterns, and they expose where the algorithm is efficient and where there is room for improvement considering tunability and scalability.