Clustering evolutionary data with an r-dominance based multi-objective evolutionary algorithm

Clustering evolutionary data (or called evolutionary clustering) has received an enormous amount of attention in recent years. A recent framework (called temporal smoothness) considers that the clustering result should depend mainly on the current data while simultaneously not deviate too much from previous ones. In this paper, evolutionary data is clustered by a multi-objective evolutionary algorithm based on r-dominance, and the corresponding algorithm is named rEvoC. The rEvoC considers the previous clustering result (or historical data) as the reference point. We propose three strategies to define the reference point and to calculate the distance between a reference point and an individual. Based on the reference point and the r-dominance relation, the search could be guided into the region, in which a solution not only could cluster the current data well, but also does not shift two much from the previous one. Additionally, the rEvoC adopts one step k-means as a local search operator to accelerate the evolutionary search. Experimental results on two different data sets are given. The experimental results demonstrate that, the rEvoC achieves better performance than the corresponding static clustering algorithm and the evolutionary k-means algorithm.