A General Multiobjective Clustering Approach Based on Multiple Distance Measures

Many conventional clustering methods have limitations to partition data sets with different structures. The reason is that the relationship of each pair of data objects in different structures is usually based on different distance measures while conventional clustering methods are often designed for an assumption distribution in Euclidean space. Most of current clustering methods have also been proposed for integrating different distance measures together, however, the weights for different distance measures are difficult to set. To alleviate this case and to generate reliable clustering results for data sets with different structures, in this paper, a novel multiple distance measures clustering method based on a multiobjective evolutionary algorithm is proposed to this problem. This approach takes two types of distance measures as multiple objective functions and optimizes them simultaneously by using a modified multiobjective evolutionary algorithm with some new strategies including initialization, crossover operator, mutation operator, and objective functions designing. Moreover, an updated approach was also proposed for detecting the correct cluster number automatically. The new approaches are applied to many datasets with spherical and irregular structures, and the results of eight artificial, four widely used and four real data sets will be exhibited in experiments. The comparisons with other clustering algorithms show that, no matter what shape dataset has, both of the proposed approaches can get satisfactory results in combining different distance measures and detecting the optimal cluster number in a single run.