Mining Top-k Covering Rule Groups for Gene Expression Data

Summary: Proposes Top-k Covering Rule Groups to mine gene-expression data per row, skipping column enumeration and yielding speedups. Presents RCBT, a classifier from top-k rule groups with a bounded set, achieving competitive accuracy and disease insights. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 3737
Venue: SIGMOD
Year: 2005
Pagerank: 5.093636e-05
Overall Rank: 12,729 | 12.67%
DOI: 10.1145/1066157.1066234

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Gao Cong (National University of Singapore; University of Edinburgh)
2. Kian-Lee Tan (National University of Singapore)
3. Anthony K.H. Tung (National University of Singapore)
4. Xin Xu (National University of Singapore)

BibTeX Citation

@inproceedings{cong_sigmod05,
        title = {{Mining Top-k Covering Rule Groups for Gene Expression Data}},
        author = {Cong, Gao and Tan, Kian-Lee and Tung, Anthony K.H. and Xu, Xin},
        series = {{SIGMOD} '05},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1066157.1066234},
        url = {https://dl.acm.org/doi/10.1145/1066157.1066234},
        year = {2005}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,862	CSV: Visualizing and Mining Cohesive Subgraphs	2008	SIGMOD	7.0665158e-05

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
27	Fast Algorithms for Mining Association Rules	1994	VLDB	0.00052255472
161	Mining Frequent Patterns without Candidate Generation	2000	SIGMOD	0.00027981772
1,535	Exploratory Mining and Pruning Optimizations of Constrained Association Rules	1998	SIGMOD	0.00010463058
12,765	FARMER: Finding Interesting Rule Groups in Microarray Datasets	2004	SIGMOD	5.093636e-05

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