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- W3169452352 abstract "Pattern set mining has been successful in discovering small sets of highly informative and useful patterns from data. To find good models, existing methods heuristically explore the twice-exponential search space over all possible pattern sets in a combinatorial way, by which they are limited to data over at most hundreds of features, as well as likely to get stuck in local minima. Here, we propose a gradient based optimization approach that allows us to efficiently discover high-quality pattern sets from data of millions of rows and hundreds of thousands of features." @default.
- W3169452352 created "2021-06-22" @default.
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- W3169452352 date "2021-08-14" @default.
- W3169452352 modified "2023-10-16" @default.
- W3169452352 title "Differentiable Pattern Set Mining" @default.
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- W3169452352 doi "https://doi.org/10.1145/3447548.3467348" @default.
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