Study on oceanic big data clustering based on incremental K-means algorithm. (30th April 2020)

• Record Type: Journal Article
• Authors: Li, Yongyi; Yang, Zhongqiang; Han, Kaixu
• Is Part Of: International journal of innovative computing and applications. Volume 11:Number 2/3(2020)
• Page Start: 89
• Page End: 95
• Publication Date: 2020-04-30
• Subjects: cluster -- K-means -- incremental -- oceanic big -- algorithm -- MATLAB -- data points -- cluster center -- distance model -- similarity; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

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.