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        Constrained Intelligent K-Means : Improving Results with Limited Previous Knowledge

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        Amorim_Clustering.pdf (PDF, 388Kb)
        Author
        de Amorim, Renato Cordeiro
        Attention
        2299/16472
        Abstract
        It is here presented a new method for clustering that uses very limited amount of labeled data, employees two pairwise rules, namely must link and cannot link and a single wise one, cannot cluster. It is demonstrated that the incorporation of these rules in the intelligent k-means algorithm may increase the accuracy of results, this is proven with experiments where the real number of clusters in the data is unknown to the method
        Publication date
        2008
        Published in
        Procs of the Second Int Conf on Advanced Engineering Computing and Applications in Sciences, 2008
        Published version
        https://doi.org/10.1109/ADVCOMP.2008.30
        Other links
        http://hdl.handle.net/2299/16472
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