Research on PCA-Kmeans++ clustering algorithm considering Spatiotemporal dimension
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...
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| Vydáno v: | 2023 2nd International Conference on 3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME) s. 195 - 201 |
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IEEE
01.06.2023
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Huang, Jiale Dai, Jingtong Li, Yanjin |
| Author_xml | – sequence: 1 givenname: Jiale surname: Huang fullname: Huang, Jiale email: 602946539@qq.com organization: Hohai University,Nanjing,China – sequence: 2 givenname: Jingtong surname: Dai fullname: Dai, Jingtong email: 483608548@qq.com organization: Hohai University,Nanjing,China – sequence: 3 givenname: Yanjin surname: Li fullname: Li, Yanjin email: 2543737907@qq.com organization: Dalian Jiaotong University,Dalian,China |
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| PublicationTitle | 2023 2nd International Conference on 3D Immersion, Interaction and Multi-sensory Experiences (ICDIIME) |
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| Snippet | Aiming at the problem that traditional clustering algorithms cannot adapt to spatiotemporal data mining, this paper proposes a new clustering algorithm... |
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| SubjectTerms | AFC Data Classification algorithms Clustering algorithms Data mining Dimensionality reduction Interference K-means PCA Spatiotemporal clustering algorithm Spatiotemporal phenomena Visualization |
| Title | Research on PCA-Kmeans++ clustering algorithm considering Spatiotemporal dimension |
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