Stakeholders quantification plays a basic role in selecting the appropriate requirements because their judgement is a major criteria since not all of them have the same importance. Original proposals quantified stakeholders assigning them a weight. Nonetheless, actual projects manage a numerous stakeholders community hindering the inclusion of all their weights. This work proposes grouping strategies as means to reduce the number of stakeholders to manage in requirements selection keeping a proper coverage (i.e. howthe selection fulfils stakeholder demands). Our approach is based on stakeholders' salience, defined in terms of power, legitimacy and urgency attributes. Diverse strategies are applied selecting important stakeholders groups in a specific project. We use k-means}, k-medoids and hierarchical clustering, after deciding the number of cluster (4 and 3) based on validation indices. Either for all the stakeholders and each important group several requirements selection optimization problems have been solved. Tests find no significant differences for coverage when important stakeholders are filtered using clustering, regardless of the technique and number of groups, with a reduction between 66.32% to 87.75% in the number of stakeholders being considered. Applying clustering methods on data obtained from a real-world project is useful to identify the group of important stakeholders. The number of groups suggested matches the stakeholders theory and the coverage values in the requirements selection is kept.

翻译：干系人量化在选择合适需求中起着基础作用，因为其判断是主要标准，并非所有干系人都具有相同的重要性。原始方案通过赋予干系人权重来进行量化。然而，实际项目管理的干系人群体庞大，难以纳入所有权重。本文提出分组策略作为降低需求选择中需管理的干系人数量、同时保持适当覆盖率（即选择如何满足干系人需求）的方法。本方法基于干系人的显著性，具体定义为权力、合法性和紧迫性三个属性。在特定项目中，我们采用多种策略选择重要的干系人群体。基于验证指标确定聚类数量（4类和3类）后，分别使用k-means、k-medoids和层次聚类。针对所有干系人及每个重要群体，均求解了多个需求选择优化问题。测试结果表明：无论采用何种聚类技术和分组数量，当使用聚类过滤重要干系人时，覆盖率未出现显著差异，且所考虑的干系人数量减少了66.32%至87.75%。对真实项目数据应用聚类方法有助于识别重要干系人群体，其建议的分组数量与干系人理论相符，且需求选择中的覆盖率值得以保持。