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- W2572500892 abstract "This paper proposes a new semi-supervised clustering framework to represent and integrate quantitative preferences on attributes. A new metric learning algorithm is derived that achieves a compromise clustering between a data-driven and a user-driven solution and converges with a good complexity. We observe experimentally that the addition of preferences may be essential to achieve a better clustering. We also show that our approach performs better than the state-of-the art algorithms." @default.
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- W2572500892 date "2016-11-01" @default.
- W2572500892 modified "2023-09-26" @default.
- W2572500892 title "Clustering with Quantitative User Preferences on Attributes" @default.
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- W2572500892 doi "https://doi.org/10.1109/ictai.2016.0065" @default.
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