Research on PCA-Kmeans++ clustering algorithm considering Spatiotemporal dimension - Huang, Jiale; Dai, Jingtong; Li, Yanjin (2023 2nd International Conference on 3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME), 2023)
Aiming at the problem that traditional clustering algorithms cannot adapt to spatiotemporal data mining, this paper proposes a new clustering algorithm PCA-Kmeans++. First, in order to reduce the interference of data dimension, an improved PCA (principal component analysis) dimensionality reduction algorithm is built. On this basis, a K-means++algorithm considering space-time dimension is proposed to cluster the reduced factors. Finally, 100000 AFC data are collected for validity verification. The results show that: (1) The improved PCA algorithm has better dimensionality reduction effect. (2) The spatiotemporal clustering algorithm based on K-means++can effectively enhance the efficiency of classification decision-making. This study provides relevant basis and methodology for proposing a generic clustering algorithm.