Abstract
Since the amount of trajectory data is large and the structure of trajectory data is complex, an improved density-based K-means algorithm was proposed. Firstly, high-density trajectory data points were selected as the initial clustering centers based on the density and increasing the density weight of important points, to perform K-means clustering. Secondly the clustering results were evaluated by the Between-Within Proportion index. Finally, the optimal clustering number and the best clustering were determined according to the clustering results evaluation. Theoretical researches and experimental results showed that the improved algorithm could be better at extracting the trajectory key points. The accuracy of clustering results was 24% points higher than that of the traditional K-means algorithm and 16% points higher than that of the Density-Based Spatial Clustering of Applications with Noise algorithm. The proposed algorithm has a better stability and a higher accuracy in trajectory data clustering.
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Acknowledgments
This research was supported by the Fundamental Research Funds for the Universities in Tianjin, Tianjin Chengjian Universities (2016CJ11)
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Hao, MW., Dai, HL., Hao, K., Li, C., Zhang, YJ., Song, HN. (2018). Optimization of Density-Based K-means Algorithm in Trajectory Data Clustering. In: Li, C., Mao, S. (eds) Wireless Internet. WiCON 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 230. Springer, Cham. https://doi.org/10.1007/978-3-319-90802-1_39
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DOI: https://doi.org/10.1007/978-3-319-90802-1_39
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