关键词: 迁移学习, 历史知识, 类中心间距极大, 隐私保护, 模糊聚类

Abstract: Traditional clustering algorithms are prone to being failure in two cases: The data are quite sparse or distorted by plenty of noise or outliers; To proportionally scale raw data in order to control the difference existing in eventual data. To address these issues, this paper first devises the history knowledge transfer as well as the maximum cluster-center-distance mechanisms, and then, combining these two mechanisms with the classical Maximum Entropy Clustering（MEC） approach, this paper proposes the center distance maximization clustering with historical knowledge transfer（HKT-CDMC for short）. In general, the major merits of HKT-CDMC are three-fold: Benefiting from the guidance of historical knowledge, HKT-CDMC proves high effectiveness in the situations where the data are insufficient or distorted by much noise; After data scaling, the cluster centers obtained by those classical clustering methods are likely to be too close, HKT-CDMC, however, can effectively avoid this phenomenon via the maximum cluster-center-distance mechanism; As the historical knowledge cannot be mapped inversely into the raw data, HKT-CDMC is of good capability of privacy protection for the source domain. The experimental studies on both artificial and real-world datasets demonstrated these merits of our work.

Key words: transfer learning, historical knowledge, maximum cluster-center-distance, privacy protection, fuzzy clustering