Abstract
This study focuses on high dimensional data, which are characterized by sparsity, redundancy, and high computational complexity. It is impossible to obtain expected results via clustering with traditional algorithms due to the “Curse of Dimensionality”. In this study, we propose a Stacked-Random Projection dimensionality reduction framework and a dimensionality reduction evaluation index based on distance preservation. The algorithm uses Stacked-Random Projection to reduce the dimensionality of the high-dimensional data, and then spectral clustering and fast search and find density peak clustering are used to cluster the processed data. The algorithm is validated using two high-dimensional data sets. Experimental results show that this algorithm can improve the performance of clustering algorithm significantly.
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Sun, Y., Platoš, J. (2021). High-Dimensional Data Clustering Algorithm Based on Stacked-Random Projection. In: Barolli, L., Li, K., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2020. Advances in Intelligent Systems and Computing, vol 1263. Springer, Cham. https://doi.org/10.1007/978-3-030-57796-4_38
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DOI: https://doi.org/10.1007/978-3-030-57796-4_38
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