State-of-the-art clustering algorithms provide little insight into the rationale for cluster membership, limiting their interpretability. In complex real-world applications, the latter poses a barrier to machine learning adoption when experts are asked to provide detailed explanations of their algorithms’ recommendations. We present a new unsupervised learning method that leverages Mixed Integer Optimization techniques to generate interpretable tree-based clustering models. Utilizing a flexible optimization-driven framework, our algorithm approximates the globally optimal solution leading to high quality partitions of the feature space. We propose a novel method which can optimize for various clustering internal validation metrics and naturally determines the optimal number of clusters. It successfully addresses the challenge of mixed numerical and categorical data and achieves comparable or superior performance to other clustering methods on both synthetic and real-world datasets while offering significantly higher interpretability.

中文翻译：

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可解释聚类：一种优化方法

最先进的聚类算法几乎无法深入了解聚类成员的基本原理，从而限制了它们的可解释性。在复杂的实际应用中，当专家被要求对其算法的建议提供详细解释时，后者对机器学习的采用构成了障碍。我们提出了一种新的无监督学习方法，该方法利用混合整数优化技术来生成可解释的基于树的聚类模型。利用灵活的优化驱动框架，我们的算法逼近导致特征空间高质量分区的全局最优解。我们提出了一种新方法，可以针对各种聚类内部验证指标进行优化，并自然地确定最佳聚类数。