NeuroRule: A Connectionist Approach to Data Mining

Summary: Introduces neural-network classification for data mining with explicit rule extraction, addressing connectionism’s interpretability gap. Extracted rules are comparable to or more concise than decision-tree rules, supported by experiments against prior methods. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 8483
Venue: VLDB
Year: 1995
Pagerank: 5.9147876e-05
Overall Rank: 6,322 | 56.63%
DOI: -

Incoming Non-self Citations Over Time

Authors

1. Hongjun Lu (National University of Singapore)
2. Rudy Setiono (National University of Singapore)
3. Huan Liu (National University of Singapore)

BibTeX Citation

@article{lu_vldb95,
        title = {{NeuroRule: A Connectionist Approach to Data Mining}},
        author = {Lu, Hongjun and Setiono, Rudy and Liu, Huan},
        journal = {PVLDB},
        series = {{VLDB} '95},
        pages = {478},
        year = {1995}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
462	Sampling Large Databases for Association Rules	1996	VLDB	0.00018065337
12,885	Decision Tables: Scalable Classification Exploring RDBMS Capabilities	2000	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
349	Knowledge Discovery in Databases: An Attribute-Oriented Approach	1992	VLDB	0.00020432737
440	An Interval Classifier for Database Mining Applications	1992	VLDB	0.00018409145

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