Improved Semi-supervised Clustering Algorithm Based on Affinity Propagation

• Published in: 东华大学学报（英文版） Vol. 32; no. 1; pp. 125 - 131
• Main Author: 金冉 刘瑞娟 李晔锋 寇春海
• Format: Journal Article
• Language: English
• Published: College of Science, Donghua University, Shanghai 201620, China 28.02.2015; College of Computer Science and Technology, Zhejiang Wanli University, Ningbo 315100, China%College of Information Science and Technology, Donghua University, Shanghai 201620, China%College of Information Science and Technology, Donghua University, Shanghai 201620, China; College of Information Science and Technology, Donghua University, Shanghai 201620, China
• Subjects: affinity propagation (AP); layered combination; computation complexity; semi-supervised clustering; combined promotion
• ISSN: 1672-5220

A clustering algorithm for semi-supervised affinity propagation based on layered combination is proposed in this paper in light of existing flaws. To improve accuracy of the algorithm,it introduces the idea of layered combination, divides an affinity propagation clustering（ APC） process into several hierarchies evenly,draws samples from data of each hierarchy according to weight,and executes semi-supervised learning through construction of pairwise constraints and use of submanifold label mapping,weighting and combining clustering results of all hierarchies by combined promotion. It is shown by theoretical analysis and experimental result that clustering accuracy and computation complexity of the semi-supervised affinity propagation clustering algorithm based on layered combination（ SAP-LC algorithm） have been greatly improved.