K-Means聚类算法及其性能优化研究

K-means Clustering Algorithm and Its Application in Data Analysis

K-Means聚类算法是一种无监督的聚类算法,实现起来相对简单,有很好的聚类效果,并具有较高的可解释性,了解K-Means聚类算法在数据分析中的具体应用至关重要。提出K-Means聚类算法的优缺点及性能优化,并结合图像处理进行应用分析,研究表明,可以被推广到相关领域进行分析应用。

The K-Means clustering algorithm is an unsupervised clustering algorithm,which is relatively simple to implement,has a good clustering effect,and has high interpretability.Understand the K-Means clustering algorithm in data analysis,the specific application is critical.The advantages and disadvantages of K-Means clustering algorithm and performance optimization are proposed,and combined with image processing for application analysis,the research shows that it can be extended to related fields for analysis and application.