Sergey Brin
Rajeev Rastogi
Kyuseok Shim
- AF92.F.D. Amore and P. G. Franciosa. On the optimal binary plane partition for sets of isothetic rectangles, information Processing Letters, 44(5):255-259, December 1992. Google Scholar
Digital Library
- AIS93.Rakesh Agrawal, Tomasz Imielinski, and Arun Swami. Mining association rules between sets of items in large databases. In Proc. of the A CM $IGMOD Conference on Management of Data, pages 207-216, Washington, D.C., May 1993. Google ScholarDigital Library
- AS94.Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules. In Proc. of the VLDB Conference, Santiago, Chile, September 1994. Google ScholarDigital Library
- FMMT96a.Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, and Takesh Tokuyama. Data mining using two-dimensional optimized association rules: Scheme, algorithms, and visualization. In Proc. of the A CM $IGMOD Conference on Management of Data, June 1996. Google ScholarDigital Library
- FMMT96b.Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, and Takesh Tokuyama. Mining optimized association rules for numeric attributes. In Proc. of the A CM SIGA CT-SiGMOD-SIGART Symposium on Principles of Database Systems, June 1996. Google ScholarDigital Library
- HF95.J. Han and Y. Fu. Discovery of multiple-level association rules from large databases. In Proc. of the VLDB Conference, Zurich, Switzerland, September 1995. Google ScholarDigital Library
- KMP98.S. Khanna, S. Muthukrishnan, and M. Paterson. On approximating rectangle tiling and packing. In Proc. 9th Annual Symposium on Discrete Algorithms (SODA), pages 384-393, 1998. Google ScholarDigital Library
- MTV94.Heikki Mannila, Hannu Toivonen, and A. Inkeri Verkamo. Efficient algorithms for discovering association rules, in KDD- 9~: AAAI Workshop on Knowledge Discovery in Databases, pages 181-192, Seattle, Washington, July 1994.Google Scholar
- PCY95.Jong Soo Park, Ming-Syan Chen, and Philip S. Yu. An effective hash based algorithm for mining association rules. In Proc. of the A CM-SIGMOD Conference on Management of Data, San Jose, California, May 1995. Google ScholarDigital Library
- RS98a.R. Rastogi and K. Shim. Mining optimized association rules for categorical and numeric attributes. In Int'l Conference on Data Engineering, Orlando, 1998. Google ScholarDigital Library
- RS98b.R. Rastogi and K. Shim. Mining optimized gain rules numeric attributes. Technical report, Bell Laboratories, Murray Hill, 1998.Google Scholar
- RS99.R. Rastogi and K. Shim. Mining optimized support rules for numeric attributes. In Int'l Conference on Data Engineering, Sydney, Austrailia, 1999. Google ScholarDigital Library
- SA95.Ramakrishnan Srikant and Rakesh Agrawal. Mining generalized association rules. In Proc. of the VLDB Conference, Zurich, Switzerland, September 1995. Google ScholarDigital Library
- SA96.Ramakrishnan Srikant and Rakesh Agrawal. Mining quantitative association rules in large relational tables. In Proc. of the A CM SIGMOD Conference on Management of Data, June 1996. Google ScholarDigital Library
- SON95.A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In Proc. of the VLDB Conference, Zurich, Switzerland, September 1995. Google ScholarDigital Library
Index Terms
- Mining optimized gain rules for numeric attributes
Recommendations
Mining Optimized Gain Rules for Numeric Attributes
Association rules are useful for determining correlations between attributes of a relation and have applications in the marketing, financial, and retail sectors. Furthermore, optimized association rules are an effective way to focus on the most ...
Mining Optimized Association Rules with Categorical and Numeric Attributes
Mining association rules on large data sets has received considerable attention in recent years. Association rules are useful for determining correlations between attributes of a relation and have applications in marketing, financial, and retail ...
Mining Association Rules on Related Numeric Attributes
PAKDD '99: Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data MiningIn practical applications, some property is represented by a pair of related attributes. For example, blood pressure, temperature changes etc. The existing data mining approaches for association rules can not tackle those cases, because they treat every ...
Comments