Association Rules over Interval Data
Summary: Interval data (ordered with meaningful gaps) breaks support/confidence as rule-adequacy; introduces a new interest definition that respects interval semantics. Presents an algorithm to mine rules under the new metric and demonstrates scalability on large real-world datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. R. J. Miller (Ohio State University)
- 2. Y. Yang (Ohio State University)
BibTeX Citation
@inproceedings{miller_sigmod97,
title = {{Association Rules over Interval Data}},
author = {Miller, R. J. and Yang, Y.},
series = {{SIGMOD} '97},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/253260.253361},
url = {https://dl.acm.org/doi/10.1145/253260.253361},
year = {1997}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 308 | Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications | 1998 | SIGMOD | 0.00021463972 |
| 1,561 | Exploratory Mining and Pruning Optimizations of Constrained Association Rules | 1998 | SIGMOD | 0.00010235581 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 13 | Mining Association Rules between Sets of Items in Large Databases | 1993 | SIGMOD | 0.00064391979 |
| 32 | BIRCH: An Efficient Data Clustering Method for Very Large Databases | 1996 | SIGMOD | 0.00049714561 |
| 94 | Efficient and Effective Clustering Methods for Spatial Data Mining | 1994 | VLDB | 0.0003456395 |
| 250 | An Effective Hash-Based Algorithm for Mining Association Rules | 1995 | SIGMOD | 0.00023145961 |
| 467 | Discovery of Multiple-Level Association Rules from Large Databases | 1995 | VLDB | 0.00017758586 |
| 475 | Sampling Large Databases for Association Rules | 1996 | VLDB | 0.00017665769 |
| 476 | Mining Generalized Association Rules | 1995 | VLDB | 0.00017664233 |
| 573 | An Efficient Algorithm for Mining Association Rules in Large Databases | 1995 | VLDB | 0.0001616577 |
| 620 | Mining Quantitative Association Rules in Large Relational Tables | 1996 | SIGMOD | 0.00015495576 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 4,446 | Online Association Rule Mining | 1999 | SIGMOD |
| 2 | 467 | Discovery of Multiple-Level Association Rules from Large Databases | 1995 | VLDB |
| 3 | 1,241 | Maintaining Data Privacy in Association Rule Mining | 2002 | VLDB |
| 4 | 476 | Mining Generalized Association Rules | 1995 | VLDB |
| 5 | 4,231 | A New SQL-like Operator for Mining Association Rules | 1996 | VLDB |
| 6 | 573 | An Efficient Algorithm for Mining Association Rules in Large Databases | 1995 | VLDB |
| 7 | 4,666 | Data Mining Using Two-Dimensional Optimized Association Rules: Scheme, Algorithms, and Visualization | 1996 | SIGMOD |
| 8 | 13 | Mining Association Rules between Sets of Items in Large Databases | 1993 | SIGMOD |
| 9 | 620 | Mining Quantitative Association Rules in Large Relational Tables | 1996 | SIGMOD |
| 10 | 4,876 | Mining Optimized Association Rules for Numeric Attributes | 1996 | PODS |