MK-Means: Detecting evolutionary communities in dynamic networks

Abstract

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.