 A Robust k -Means Clustering Algorithm Based on Observation Point MechanismXiaoliang Zhang, Yulin He, Yi Jin, Honglian Qin, Muhammad Azhar and Joshua Zhexue Huang Complexity, 2020, vol. 2020, 1-11 Abstract: The k -means algorithm is sensitive to the outliers. In this paper, we propose a robust two-stage k -means clustering algorithm based on the observation point mechanism, which can accurately discover the cluster centers without the disturbance of outliers. In the first stage, a small subset of the original data set is selected based on a set of nondegenerate observation points. The subset is a good representation of the original data set because it only contains all those points that have a higher density of the original data set and does not include the outliers. In the second stage, we use the k -means clustering algorithm to cluster the selected subset and find the proper cluster centers as the true cluster centers of the original data set. Based on these cluster centers, the rest data points of the original data set are assigned to the clusters whose centers are the closest to the data points. The theoretical analysis and experimental results show that the proposed clustering algorithm has the lower computational complexity and better robustness in comparison with k -means clustering algorithm, thus demonstrating the feasibility and effectiveness of our proposed clustering algorithm. Date: 2020 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:3650926 DOI: 10.1155/2020/3650926 Access Statistics for this article More articles in Complexity from Hindawi |
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