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• W2735871569 abstract "Multi-View Clustering models can be viewed as a way to extract information from different data representations to improve the clustering accuracy. In multi-view clustering, some views are irrelevant and among the relevant ones, some may be more or less relevant than others. This is why the most part of existing algorithms assign a weight to each view aiming to compute its relevance in the clustering process. However very few algorithms computes also the relevance weight of variables inside each view aiming to achieve automated feature selection. This paper proposes a muti-view hard c-means clustering algorithm with automated computation of weights for both views and variables in such a way that the relevant views as well as the relevant variables in each view are selected for clustering. Compared to previous similar works, an advantage of the proposed method is that, apart the need to know previously the number of clusters, there are no additional parameters to tune. Experiments with benchmark data sets corroborate the usefulness of the proposed method." @default.
• W2735871569 created "2017-07-21" @default.
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• W2735871569 date "2017-05-01" @default.
• W2735871569 modified "2023-09-26" @default.
• W2735871569 title "Multi-view hard c-means with automated weighting of views and variables" @default.
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• W2735871569 doi "https://doi.org/10.1109/ijcnn.2017.7966200" @default.
• W2735871569 hasPublicationYear "2017" @default.
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