Abstract: Various problems such as sensitive selection of initial clustering center, easily falling into local optimal solution, and determining numbers of clusters, still exist in the traditional clustering algorithm. A GEP automatic clustering algorithm with dynamic penalty factors was proposed. This algorithm combines penalty factors and GEP clustering algorithm, and doesn't rely on any priori knowledge of the data set. And a dynamic algorithm was proposed to generate the penalty factors according to the distribution characteristics of different data sets, which is a better solution for the impact of isolated points and noise points. According to four dataset, penalty factors' effect was tested. Base on the result, a formula to generate penalty factors was proposed. The penalty factor calculated from the formula was used in clustering of the standard data set Iris. The experimental result shows that the efficiency and accuracy of the algorithm are good.

Key words: dynamic penalty factor, GEP, clustering algorithm, machine learning