An improved incomplete AP clustering algorithm based on K nearest neighbours. (23rd April 2019)

• Record Type: Journal Article
• Authors: Chen, Zhikui; Leng, Yonglin; Hu, Yueming
• Is Part Of: International journal of embedded systems. Volume 11:Number 3(2019)
• Page Start: 269
• Page End: 277
• Publication Date: 2019-04-23
• Subjects: incomplete data -- affinity propagation clustering -- K nearest neighbours -- incomplete information system; Embedded computer systems -- Periodicals; 004.16
• Languages: English
• ISSNs: 1741-1068

Abstract

With the fast development of internet of things (IoT), a large amount of missing data is produced in the process of data collection and transmission. We call these data incomplete data. Many traditional methods use imputation or discarding strategy to cluster incomplete data. In this paper, we propose an improved incomplete affinity propagation (AP) clustering algorithm based on K nearest neighbours (IAPKNN). IAPKNN firstly partitions the dataset into complete and incomplete dataset, and then clusters the complete data set by AP clustering directly. Secondly, according to the similarity, IAPKNN extends the responsibility and availability matrices to the incomplete dataset. Finally, clustering algorithm is restarted based on the extended matrices. In addition, to address the clustering efficiency of large scale dataset, we give a distributed clustering algorithm scheme. Experiment results demonstrate that IAPKNN is effective in clustering incomplete data directly.