Enhanced K-means re-clustering over dynamic networks

Abstract

This paper presents a preliminary algorithm which is designed to reduce the processing cost of continuing clustering in dynamic networks. This algorithm considers that various types of changes (Inserts and deletes) might affect the clustered data over time. It promises to provide both a reliable and updated answer for clustering problem at all times. By altering the well-known K-means algorithm, this enhanced version has three parts: Initializer and Sorter, the main objective of this part is to initialize the algorithm and to store some data that can be used to reduce the calculations later on, The Dynamic Modifier, this part applies the modifications on clusters and also updates the centroids and the related info to keep the clusters valid, and The Detector, which detects the potent nodes which might need to swap their clusters after applying the recent changes that Dynamic Modifier applied. This algorithm reduces the amount of calculations by using the related data from the last scope of the clustered network to detect the potent nodes, so it can only check them for further modifications. The simulation results indicate that the number of checked nodes and the total consumed time during each iteration is reduced significantly comparing to the traditional K-means algorithm.