To improve the accuracy and computational efficiency of the MapReduce distributed parallel computing framework, thereby mining the diagnosis and treatment data of Kashin-Beck Disease (KBD) of the knee joint. Based on the shortcomings of the traditional K-means Clustering Algorithm (KCA), a simplified method for distance calculation was proposed. The Manhattan distance was used instead of Euclidean distance. Further improvement strategies were proposed to implement and compare KCA of MapReduce (MR-KCA) and Improved MR-KCA (IMR-KCA). With the same data, the sum of squared errors of MR-KCA and IMR-KCA decreased with the increase in the number of center points. Compared with MR-KCA, the quality of IMR-KCA was higher, and their difference was especially evident at 8 GB data capacity. The total execution time of both MR-KCA and IMR-KCA increased with the increase in the number of center points. Compared to MR-KCA, the total execution time of IMR-KCA was significantly reduced, especially when the data capacity was 8 GB. When the number of center points was 5000, IMR-KCA could reduce the total execution time by 50%. Through experiments, IMR-KCA was proved to better present the diagnosis and treatment data of patients with knee joint KBD. The scalability rates of MR-KCA and IMR-KCA decreased as the number of nodes increased, but the scalability rates of both algorithms could be maintained above 0.80, which had better scalability. Compared with MR-KCA, IMR-KCA had significantly higher scalability. The IMR-KCA proposed in this study had high accuracy and computing efficiency, which could be used in the visualization of KBD diagnosis and treatment.

为了提高MapReduce分布式并行计算框架的准确性和计算效率，从而挖掘膝关节Kashin-Beck病（KBD）的诊断和治疗数据。针对传统的K均值聚类算法（KCA）的不足，提出了一种简化的距离计算方法。使用曼哈顿距离代替欧几里得距离。提出了进一步的改进策略，以实现和比较MapReduce的KCA（MR-KCA）和改进的MR-KCA（IMR-KCA）。在相同数据的情况下，MR-KCA和IMR-KCA的平方误差总和随着中心点数量的增加而减小。与MR-KCA相比，IMR-KCA的质量更高，并且在8 GB数据容量时它们的差异尤为明显。MR-KCA和IMR-KCA的总执行时间随着中心点数量的增加而增加。与MR-KCA相比，IMR-KCA的总执行时间大大减少，尤其是当数据容量为8 GB时。当中心点数为5000时，IMR-KCA可以将总执行时间减少50％。通过实验证明，IMR-KCA可以更好地呈现膝关节KBD患者的诊断和治疗数据。MR-KCA和IMR-KCA的可扩展性随着节点数量的增加而降低，但是两种算法的可扩展性都可以保持在0.80以上，具有更好的可扩展性。与MR-KCA相比，IMR-KCA具有更高的可扩展性。这项研究提出的IMR-KCA具有很高的准确性和计算效率，