Dynamic clustering using multi-objective evolutionary algorithm

A new dynamic clustering method using multi-objective evolutionary algorithm is proposed. As opposed to the traditional static clustering algorithms, our method implements variable length chromosome which allows the algorithm to search for both optimal cluster center positions and cluster number. Thus the cluster number is optimized during run time dynamically instead of being pre-specified as a parameter. We also introduce two complementary objective functions–compactness and connectedness instead of one single objective. To optimize the two measures simultaneously, the NSGA-II, a highly efficient multi-objective evolutionary algorithm, is adapted for the clustering problem. The simultaneous optimization of these objectives improves the quality of the resulting clustering of problems with different data properties. At last, we apply our algorithm on several real data sets from the UCI machine learning repository and obtain good results.