A Fast Multiobjective Fuzzy Clustering with Multimeasures Combination

Abstract EN

Most of the existing clustering algorithms are often based on Euclidean distance measure.

However, only using Euclidean distance measure may not be sufficient enough to partition a dataset with different structures.

Thus, it is necessary to combine multiple distance measures into clustering.

However, the weights for different distance measures are hard to set.

Accordingly, it appears natural to keep multiple distance measures separately and to optimize them simultaneously by applying a multiobjective optimization technique.

Recently a new clustering algorithm called ‘multiobjective evolutionary clustering based on combining multiple distance measures’ (MOECDM) was proposed to integrate Euclidean and Path distance measures together for partitioning the dataset with different structures.

However, it is time-consuming due to the large-sized genes.

This paper proposes a fast multiobjective fuzzy clustering algorithm for partitioning the dataset with different structures.

In this algorithm, a real encoding scheme is adopted to represent the individual.

Two fuzzy clustering objective functions are designed based on Euclidean and Path distance measures, respectively, to evaluate the goodness of each individual.

An improved evolutionary operator is also introduced accordingly to increase the convergence speed and the diversity of the population.

In the final generation, a set of nondominated solutions can be obtained.

The best solution and the best distance measure are selected by using a semisupervised method.

Afterwards, an updated algorithm is also designed to detect the optimal cluster number automatically.

The proposed algorithms are applied to many datasets with different structures, and the results of eight artificial and six real-life datasets are shown in experiments.

Experimental results have shown that the proposed algorithms can not only successfully partition the dataset with different structures, but also reduce the computational cost.