Mining Non-Redundant High Order Correlations in Binary Data

Summary: Introduces Non-redundant Interacting Feature Subsets (NIFS) to uncover high-order, non-redundant correlations in binary data via multi-information. Develops properties, upper/lower bounds, and a pairwise MI pruning strategy to trim the search space; validates efficiency on synthetic and real datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 9745
Venue: VLDB
Year: 2008
Pagerank: 4.5652868e-05
Overall Rank: 8,185 | 43.06%
DOI: -

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,755	Multivariate Correlations Discovery in Static and Streaming Data	2022	VLDB	4.456315e-05

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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