Most cluster analysis parameters in atom probe tomography (APT) are selected ad hoc. This can often lead to data misinterpretation and misleading results by instrument technicians and researchers. Moreover, arbitrary cluster parameters can have suboptimal consequences on data quality and integrity, leading to inefficiencies for downstream data users. To address these problems, researchers at the University of California, Berkeley, have developed a framework and specific cluster analysis methods to efficiently extract knowledge from better APT data. By using parameter selection protocols with theoretical explanations, this technology allows for a more optimized and robust multivariate statistical analysis technique from the start, thus improving the quality of analysis and outcomes for both upstream and downstream data users.