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More than two hundred papers on elicitation studies have been published in the last ten years. These works are mainly focused on generating user-defined gesture sets and discovering natural feeling multimodal interaction techniques with virtual objects. Few papers have discussed binning the elicited interaction proposals after data collection. Binning is a process of grouping the entire set of user-generated interaction proposals based on similarity criteria. The binned set of proposals is then analyzed to produce a consensus set, which results in the user-defined interaction set. This paper presents a formula to use when deciding how to bin interaction proposals, thus helping to establish a more consistent binning procedure. This work can provide human-computer interaction (HCI) researchers with the guidance they need for interaction elicitation data processing, which is largely missing from current elicitation study literature. Using this approach will improve the efficiency and effectiveness of the binning process, increase the reliability of us er-defined interaction sets, and most importantly, improve the replicability of elicitation studies.