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        Instance Weighted Clustering: Local Outlier Factor and K-Means

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        loiwreport2_final_submission.pdf (PDF, 460Kb)(embargoed until 28/05/2022)
        Author
        Moggridge, Paul
        Helian, Na
        Sun, Yi
        Lilley, Mariana
        Veneziano, Vito
        Attention
        2299/23248
        Abstract
        Clustering is an established unsupervised learning method. Substantial research has been carried out in the area of feature weighting, as well instance selection for clustering. Some work has paid attention to instance weighted clustering algorithms using various instance weighting metrics based on distance information, geometric information and entropy information. However, little research has made use of instance density information to weight instances. In this paper we use density to define instance weights. We propose two novel instance weighted clustering algorithms based on Local Outlier Factor and compare them against plain k-means and traditional instance selection.
        Publication date
        2020-05-28
        Published in
        Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference
        Published version
        https://doi.org/10.1007/978-3-030-48791-1_34
        Other links
        http://hdl.handle.net/2299/23248
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