Trust-Similarity Analysis-Based Clustering Method

Opinion similarity and trust relationship are considered to be two important measurement attributes for implementing clustering. Traditional clustering methods often use a single attribute to divide the original large group without requiring a combination of the above two attributes. However, these two attributes play different roles in the clustering process, insofar as opinion similarity is used to measure the level of difference among individual opinions, whereas the trust relationship represents the trustworthiness of decision makers. This chapter proposes a trust-similarity analysis (TSA)-based clustering method to implement clustering in LSGDM events under a social network context. First, the trust-similarity matrix is established to collectively describe the decision information. Second, all measurement attribute values are mapped to a trust-similarity plot from which the joint threshold can be calculated. Finally, a TSA-based clustering method is proposed that considers the attributes of opinion similarity and trust relationship and that allocates their importance to achieve specific clustering objectives. The numerical experiment and comparative analysis reveal the feasibility and advantages of the proposed method.