The increasing demand for high product quality by consumers poses new challenges to the efficiency and effectiveness of manufacturers’ quality problem-solving. Solving problems based on personal experience makes problem-solving inefficient and ineffective. However, the data recorded during the problem-solving process can offer valuable experiential knowledge for problem-solvers. In this study, we propose a bipartite graph clustering method for discovering the knowledge of short-term remedies, which is a type of solution, from past quality problem-solving data. In this method, several clustering algorithms are compared, and the K-means algorithm is selected to cluster quality problems into typical problem clusters. A novel two-stage clustering method based on verb and noun clustering is then developed to construct typical short-term remedy clusters. Based on the clustering result, the relationship between problem clusters and short-term remedy clusters is generated. A reasoning method for extracting short-term remedy knowledge to solve new problems is introduced, and quality problem-solving data on an automobile manufacturer are used to carry out a case study. Tools such as Gephi and a prototype system are applied to provide “problem cluster–short-term remedy cluster” knowledge. Problem-solvers can use this knowledge to quickly address new problems, thereby improving the efficiency and effectiveness of product quality problem-solving.

消费者对高质量产品不断增长的需求，对制造商解决质量问题的效率和有效性提出了新的挑战。根据个人经验解决问题会使解决问题的效率低下和无效。但是，在问题解决过程中记录的数据可以为问题解决者提供宝贵的经验知识。在这项研究中，我们提出了一种二部图聚类方法，用于从过去的质量问题解决数据中发现短期补救措施的知识，这是一种解决方案。在这种方法中，比较了几种聚类算法，并选择了K-means算法将质量问题聚类为典型的问题聚类。然后，提出了一种基于动词和名词聚类的新型两阶段聚类方法，以构建典型的短期补救聚类。根据聚类结果，生成问题聚类和短期补救聚类之间的关系。介绍一种提取短期补救措施知识以解决新问题的推理方法，并使用汽车制造商的质量问题解决数据进行案例研究。应用诸如Gephi和原型系统之类的工具来提供“问题集群-短期补救集群”知识。解决问题的人可以利用这些知识快速解决新问题，从而提高解决产品质量问题的效率和有效性。并使用汽车制造商的质量问题解决数据进行案例研究。应用诸如Gephi和原型系统之类的工具来提供“问题集群-短期补救集群”知识。解决问题的人可以利用这些知识快速解决新问题，从而提高解决产品质量问题的效率和有效性。并使用汽车制造商的质量问题解决数据进行案例研究。应用诸如Gephi和原型系统之类的工具来提供“问题集群-短期补救集群”知识。解决问题的人可以利用这些知识快速解决新问题，从而提高解决产品质量问题的效率和有效性。