Abstract
In this paper, a sampled-based version of the visual assessment of cluster tendency (VAT) algorithm [2] for large datasets is presented. The proposed algorithm consists of two main steps. We first propose a postprocessing task of the ProTraS algorithm [9] to obtain a sample of the dataset such that clusters in the sample are separated as much as possible while preserving the cluster structure of the whole dataset. The second one is to apply iVAT [5] on the sample to display the cluster tendency of the whole dataset. Algorithms are implemented. Numerical results are given and compared with siVAT to demonstrate the efficiency of our algorithm.
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This research is funded by Ho Chi Minh City University of Technology – VNU-HCM under grant number To-KHMT-2017-09.
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Trang, L.H., Van Ngoan, P., Van Duc, N. (2018). A Sample-Based Algorithm for Visual Assessment of Cluster Tendency (VAT) with Large Datasets. In: Dang, T., Küng, J., Wagner, R., Thoai, N., Takizawa, M. (eds) Future Data and Security Engineering. FDSE 2018. Lecture Notes in Computer Science(), vol 11251. Springer, Cham. https://doi.org/10.1007/978-3-030-03192-3_11
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DOI: https://doi.org/10.1007/978-3-030-03192-3_11
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