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- W2095496553 abstract "Feature selection becomes crucial when exploring high-dimensional datasets via clustering, because it is unlikely that the data groups jointly in all dimensions but clustering algorithms treat all attributes equally. A new subspace filter approach is presented that is capable of coping with the difficult situation of finding small clusters embedded in a very noisy environment (more noise than clustering data), which is not mislead by dense, high-dimensional spots caused by density fluctuations of single attributes. Experimental evaluation on artificial and real datasets demonstrate good performance and high efficiency." @default.
- W2095496553 created "2016-06-24" @default.
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- W2095496553 date "2014-06-30" @default.
- W2095496553 modified "2023-10-16" @default.
- W2095496553 title "A subspace filter supporting the discovery of small clusters in very noisy datasets" @default.
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- W2095496553 doi "https://doi.org/10.1145/2618243.2618260" @default.
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