In this work, new efficient and robust clustering algorithms for large datasets of mixed-type data are proposed and implemented in a new Python package called db_robust_clust. Their performance is analyzed in rather complex mixed-type datasets, where a wide variety of scenarios is considered regarding size, dimensionality, number of clusters, cluster separation, proportion and type of outlying units, cluster sphericity and imbalance, as well as several interesting interactions. The simulation study comprises extensive computational experiments starting from a baseline configuration (control scenario) in order to evaluate the robustness and efficiency of the algorithms to the progressive degradation of the ideal baseline conditions. Their performance is compared to that of state-of-the-art clustering alternatives. Finally, the goodness and computing time of the methods under evaluation are tested on real datasets of varying sizes and patterns.
