Stakeholders Classification System Based on Clustering Techniques

Abstract

Stakeholder classification is carried out by project managers using methods such as interviews with experts, brainstorming and checklists. These methods are carried out manually and present a subjective character as they depend on the appreciation of the interviewees. It affects the accuracy of the classification and the making-decisions. The objective of this research is to propose a fuzzy inference system for the classification of stakeholders, which will improve the quality of such classification in the projects. The proposal performs the automatic learning and the adjustment of the fuzzy inference system to classify the stakeholders executing two clustering algorithms: SBC and DENFIS. It examines the results of applying them in 10 iterations by calculating the measures: accuracy, false positive cases, false negative cases, mean square error and symmetric mean absolute percentage error. The best results are shown by the SBC algorithm. The fuzzy inference system for stakeholder’s classification generated improves the quality of this classification as well as the tools to support decision-making in organizations oriented to projects.