Title: An improved incomplete AP clustering algorithm based on K nearest neighbours

Authors: Zhikui Chen; Yonglin Leng; Yueming Hu

Addresses: School of Software Technology, Dalian University of Technology, Dalian 116620, China ' School of Software Technology, Dalian University of Technology, Dalian 116620, China; College of Information Science and Technology, Bohai University, Jinzhou 112100, China ' College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China

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.

Keywords: incomplete data; affinity propagation clustering; K nearest neighbours; incomplete information system.

DOI: 10.1504/IJES.2019.099413

International Journal of Embedded Systems, 2019 Vol.11 No.3, pp.269 - 277

Received: 20 Mar 2017
Accepted: 15 Jun 2017
Published online: 02 May 2019 *

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