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: 9936
Venue: VLDB
Year: 2008
Pagerank: 5.4279058e-05
Overall Rank: 8,421 | 42.23%
DOI: 10.14778/1453856.1453981

Incoming Non-self Citations Over Time

Authors

1. Xiang Zhang (University of North Carolina)
2. Feng Pan (University of North Carolina)
3. Wei Wang (University of North Carolina)
4. Andrew Nobel (University of North Carolina)

BibTeX Citation

@article{zhang_vldb08,
        title = {{Mining Non-Redundant High Order Correlations in Binary Data}},
        author = {Zhang, Xiang and Pan, Feng and Wang, Wei and Nobel, Andrew},
        journal = {PVLDB},
        series = {{VLDB} '08},
        volume = {1},
        number = {1},
        pages = {1178--1189},
        doi = {10.14778/1453856.1453981},
        url = {https://doi.org/10.14778/1453856.1453981},
        year = {2008}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,925	Multivariate Correlations Discovery in Static and Streaming Data	2022	VLDB	5.3483178e-05

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank

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