The growing complexity of integrated systems makes root-cause analysis increasingly difficult. To address this challenge, advances in machine learning (ML) have been leveraged in recent years to design ML-based techniques for root-cause analysis. However, most of these methods require root-cause labels for defective samples obtained based on the analysis by human experts. In this article, we propose a multialgorithm two-stage clustering method with transfer learning for unsupervised root-cause analysis. First, a two-stage clustering method is proposed by applying multiple clustering methods to accommodate both numerical and categorical data and leveraging Silhouette score for model selection. Next, a double-bootstrapping method is proposed for data selection, transferring valuable information from a source product to a target product with insufficient data. In the first bootstrapping step, a random forest model is built to select effective source data. In the second bootstrapping step, clustering ensemble is applied to two-stage clustering to further improve the accuracy for root-cause analysis. Two case studies based on network products demonstrate the superior performance of the proposed approach compared to other state-of-the-art methods.

中文翻译：

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集成系统迁移学习的无监督两阶段根本原因分析

集成系统的日益复杂使得根本原因分析变得越来越困难。为了应对这一挑战，近年来机器学习 (ML) 的进步被用来设计基于 ML 的根本原因分析技术。然而，这些方法中的大多数都需要对根据人类专家的分析获得的缺陷样本进行根本原因标签。在本文中，我们提出了一种具有迁移学习的多算法两阶段聚类方法，用于无监督的根本原因分析。首先，通过应用多种聚类方法来适应数值和分类数据并利用 Silhouette 分数进行模型选择，提出了一种两阶段聚类方法。接下来，提出了一种用于数据选择的双引导方法，将有价值的信息从源产品转移到数据不足的目标产品。在第一个引导步骤中，构建随机森林模型以选择有效的源数据。在第二个引导步骤中，将聚类集成应用于两阶段聚类，以进一步提高根本原因分析的准确性。基于网络产品的两个案例研究证明了与其他最先进的方法相比，所提出的方法具有优越的性能。