Path: Top -> Journal -> Jurnal Internasional -> King Saud University -> 2021 -> Volume 33, Issue 6, July
Initial seed selection for K-modes clustering A distance and density based approach
Oleh : S.A. Sajidha, Siddha Prabhu Chodnekar, Kalyani Desikan, King Saud University
Dibuat : 2022-02-14, dengan 0 file
Keyword : K modes, Clustering, Classification, Initial seed artefact, Density, Distance
Url : http://www.sciencedirect.com/science/article/pii/S1319157818300065
Sumber pengambilan dokumen : web
Initial seed artefacts play a vital role in proper categorization of the given data set in partitioning based clustering algorithms. Hence, it is important to identify them. We propose a density with distance based method which ensures identification of seed artefacts from different clusters that leads to more accurate clustering results. Our algorithm improves on the search for initial seed artefacts iteratively until the minimum value of the sum of within sum errors, normalized by their data sizes, is ensured. This is because the initial artefacts are selected from different clusters. Here the choice of seed artefacts guarantees a global optimum clustering solution. We have compared our results with random, Wu, Cao and Khans methods of initial seed artefact selection, to show the efficacy of our method.
Deskripsi Alternatif :Initial seed artefacts play a vital role in proper categorization of the given data set in partitioning based clustering algorithms. Hence, it is important to identify them. We propose a density with distance based method which ensures identification of seed artefacts from different clusters that leads to more accurate clustering results. Our algorithm improves on the search for initial seed artefacts iteratively until the minimum value of the sum of within sum errors, normalized by their data sizes, is ensured. This is because the initial artefacts are selected from different clusters. Here the choice of seed artefacts guarantees a global optimum clustering solution. We have compared our results with random, Wu, Cao and Khans methods of initial seed artefact selection, to show the efficacy of our method.
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