A density-based evolutionary clustering algorithm for intelligent development

Abstract Inspired by the clustering mechanism of human cognitive development, this paper proposes a density-based evolutionary clustering algorithm based on incremental data (DBEC). The DBEC algorithm is developed from a zero sample state without prior knowledge, and the control parameter can evolve as the number of samples increases, which simulates the human cognitive development process structurally. Furthermore, we propose conservative, robust, and radical DBEC algorithms based on different combinations of strategies. These three types of DBEC algorithms have different characteristics, and can find various clustering attributes of the samples to be clustered. Finally, we analyse the performance of three types of DBEC algorithms and compare their result with those of other popular clustering algorithms on the datasets in the experiment. The results show the effectiveness of DBEC algorithms.