Personalized Trust Management in Decision Making: A Dynamic Clustering Approach

Abstract

This paper presents a personalized approach for distributed trust management by employing the k-means range algorithm, a combination of the partitional k-means clustering algorithm with orthogonal range search concepts. The aim of this approach is to aid the human or computer agent in organizing information from multiple sources into clusters according to its “trust features”. Thus the agent can perform complicated trust assessments in real-time situations and cooperate with decision-making software to assist in purchasing activities. We conclude by discussing the implications and advantages of this approach in trust management in traditional and mobile e-commerce applications.