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- W765381642 abstract "Sampling a dataset for faster analysis and looking at it as a sample from an unknown distribution are two faces of the same coin. We discuss the use of modern techniques involving the Vapnik-Chervonenkis (VC) dimension to study the trade-off between sample size and accuracy of data mining results that can be obtained from a sample. We report two case studies where we and collaborators employed these techniques to develop efficient sampling-based algorithms for the problems of betweenness centrality computation in large graphs and extracting statistically significant Frequent Itemsets from transactional datasets." @default.
- W765381642 created "2016-06-24" @default.
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- W765381642 date "2014-01-01" @default.
- W765381642 modified "2023-09-25" @default.
- W765381642 title "Sampling-Based Data Mining Algorithms: Modern Techniques and Case Studies" @default.
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- W765381642 doi "https://doi.org/10.1007/978-3-662-44845-8_48" @default.
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