K-Means algorithm is probably the most famous and popular clustering algorithm in the world. K-Means algorithm has the advantages of simple structure, easy implementation, high efficiency, fast convergence speed, and good results. It has been widely used in many applications, and many extensions of K-Means have been proposed. Basically, most K-Means variants deal with static data. Recently, the dynamic nature of data has received increasing attention from researchers. Therefore, some studies also use K-Means algorithm to deal with clustering problems in evolutionary data. In this article, we aim to improve past variants of K-Means used in evolutionary clustering. There are two ways to improve this problem. First, past research only considered how the previous clustering results affected the current clustering, but we also considered how the future clustering results affect the current clustering. Secondly, past research applied K-Means from one cycle to another in one pass, but we extended it to multiple passes. These two improvements make the proposed algorithm MK-Means provide more consistent, stable and smooth clustering results than previous models.

K-Means算法可能是世界上最著名和最受欢迎的聚类算法。K-Means算法具有结构简单，易于实现，效率高，收敛速度快，效果好的优点。它已被广泛用于许多应用中，并且已经提出了K-Means的许多扩展。基本上，大多数K-Means变体都处理静态数据。近来，数据的动态性质受到研究人员的越来越多的关注。因此，一些研究还使用K-Means算法来处理进化数据中的聚类问题。在本文中，我们旨在改进进化聚类中使用的K-Means的过去变体。有两种方法可以改善此问题。首先，过去的研究仅考虑了先前的聚类结果如何影响当前的聚类，但是我们还考虑了未来的聚类结果如何影响当前的聚类。其次，过去的研究在一遍中将K-Means从一个周期应用于另一个周期，但我们将其扩展到了多遍。这两个改进使得所提出的算法MK-Means比以前的模型提供了更加一致，稳定和平滑的聚类结果。