In this study, a new approach to Fine-Kinney risk assessment method is developed in order to overcome the limitations of the conventional method with clustering algorithms. New risk level of classes are attempted to determine with K-Means and Hierarchical clustering algorithms with using two different distance functions which are Euclidean and Manhattan distances. According to the results, K-Means algorithms have provided accurate and sensitive cluster of classes. Classes from conventional and K-Means algorithms are applied and compared to the identified risks of a workshop of a medium sized textile company. Results of the study indicate that clustering techniques are new, original and applicable way to define new classes in order to prioritize risks by overcoming the drawbacks of conventional Fine-Kinney method.

中文翻译：

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一种新的基于聚类方法的 Fine-Kinney 方法

在本研究中，开发了一种新的 Fine-Kinney 风险评估方法，以克服传统聚类算法方法的局限性。尝试通过使用欧几里得距离和曼哈顿距离这两种不同距离函数的 K-Means 和分层聚类算法来确定类的新风险级别。根据结果​​，K-Means 算法提供了准确和敏感的类簇。应用来自传统和 K-Means 算法的类，并与中型纺织公司车间的已识别风险进行比较。研究结果表明，聚类技术是一种新的、原始的和适用的方式来定义新的类别，以便通过克服传统 Fine-Kinney 方法的缺点来确定风险的优先级。