Clustering as an essential technique has matured into a capable solution to address the gap between the growing availability of data and deriving the knowledge from them. In this paper, we propose a novel clustering method “variational learning of infinite multivariate Beta mixture models.” The motivation behind proposing this technique is the flexibility of mixture models to fit the data. This approach has the capability to infer the model complexity and estimate model parameters from the observed data automatically. Moreover, as a label‐free method, it could also address the problem of high costs of medical data labeling, which can be undertaken just by medical experts. The performance of the model is evaluated on real medical applications and compared with other similar alternatives. We demonstrate the ability of our proposed method to outperform widely used methods in the field as it has been shown in experimental results.

中文翻译：

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医学应用中多元β混合物模型的非参数变异学习

群集作为一项必不可少的技术，已经发展成为一种功能强大的解决方案，可以解决日益增长的数据可用性与从数据中获取知识之间的鸿沟。在本文中，我们提出了一种新颖的聚类方法“无限多元Beta混合模型的变异学习”。提出此技术的动机是混合模型适应数据的灵活性。这种方法具有推断模型复杂性并自动从观察到的数据估计模型参数的能力。此外，作为一种无标签的方法，它还可以解决医疗数据标签成本高昂的问题，而这只能由医学专家来承担。该模型的性能在实际医疗应用中进行了评估，并与其他类似替代方案进行了比较。