Title: Study on oceanic big data clustering based on incremental K-means algorithm

Authors: Yongyi Li; Zhongqiang Yang; Kaixu Han

Addresses: The Key Laboratory for Advanced Technology to Internet of Things, The College of Electronics and Information Engineering, Qinzhou University, Guangxi, China ' The Key Laboratory for Advanced Technology to Internet of Things, The College of Electronics and Information Engineering, Qinzhou University, Guangxi, China ' The Key Laboratory for Advanced Technology to Internet of Things, The College of Electronics and Information Engineering, Qinzhou University, Guangxi, China

Abstract: With the increase of marine industry in the Beibu Gulf, data clustering has become an important task of intelligent ocean. Partition clustering methods are suitable for marine data. However, traditional K-means algorithm is not suitable for large scale data. Focusing on the characteristics of oceanic big data, we propose a clustering method based on incremental K-means (IKM) algorithm. First, a vector model is adopted to represent data sets, and the calculation model for mean values and centres is used to initialise arbitrary numbers of data points. Second, the input data vectors are iteratively calculated in an incremental vector form. Finally, by applying incremental vector and distance model, the large-scale data are clustered according to convergence condition. Experiments show that the algorithm can increase the clustering efficiency, reduce time and space complexity, and lower the missing data rate.

Keywords: cluster; K-means; incremental; oceanic big; algorithm; MATLAB; data points; cluster center; distance model; similarity.

DOI: 10.1504/IJICA.2020.107119

International Journal of Innovative Computing and Applications, 2020 Vol.11 No.2/3, pp.89 - 95

Received: 04 Mar 2019
Accepted: 29 Apr 2019

Published online: 30 Apr 2020 *

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