Evolutionary Parameter-Free Clustering Algorithm

Abstract

The performance of the clustering algorithms depends mainly on the setting of artificial parameter values which is usually difficult in practical application. In addition, the dataset is usually incremental, and the clustering algorithm applied to the static dataset cannot develop with the change of the dataset. If new sample points are added, algorithm parameters need to be readjusted to cluster again, leading to a great time cost. This paper proposed an evolutionary parameter-free clustering algorithm (EPFC) for the above problems, which imitates the human clustering mechanism of objective things. EPFC algorithm takes the average distance between each sample and its nearest neighbour sample as the threshold value to judge whether the sample can be grouped into one cluster. The threshold value is adaptively updated without setting an artificially parameter value as the samples increase. A large number of experiments on benchmark datasets show that EPFC is effective on datasets with different characteristics, and the algorithm has strong robustness.