Mika Klemettinen
ABSTRACT
Association rules, introduced by Agrawal, Imielinski, and Swami, are rules of the form “for 90% of the rows of the relation, if the row has value 1 in the columns in set W, then it has 1 also in column B”. Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules can, however, be so large that browsing the rule set and finding interesting rules from it can be quite difficult for the user. We show how a simple formalism of rule templates makes it possible to easily describe the structure of interesting rules. We also give examples of visualization of rules, and show how a visualization tool interfaces with rule templates.
- 1.Rakesh Agrawal, Tomasz Imielinski, and Arun Swami. Mining association rules between sets of items in large databases. In Proceedings o/ the 1993 Internatzonal ConJerence on Management of Data (SIGMOD 93), pages 207- 216, May 1993. Google Scholar
Digital Library
- 2.Rakesh Agrawal and Ramakrishnan Srikant. Fast algorithms for mining association rules in large databases. In VLDB '94. September 1994. Google ScholarDigital Library
- 3.G.H. Collier. Thot-II: Hypertext with explicit semantics. In ACM Hypertext '87 Proceedings, pages 269 - 289, Chapel Hill, North Carolina, November 1987. Google ScholarDigital Library
- 4.E. It. Gansner, S. C. North, and K. P. Vo. DAG a program that draws directed graphs. Software-- Practice and Experience, 18(11):1047- 1062, November 1988. Google ScholarDigital Library
- 5.Jiawei Hail, Yandong Cai, and Nick Cercone. Knowledge discovery in databases: an attribute-oriented approach, in Proceedings of the 18th Internatzonal Conference on Very Large Databases (VLDB), pages 547- 559, August 1992. Google ScholarDigital Library
- 6.Peter H oschka and Willi K15sgen. A support system for interpreting statistical data. In Gregory Piatetsky- Shapiro and William J. Frawley, editors, Knowledge Dzscovery in Databases, pages 325 - 345. AAAI Press / The MIT Press, Menlo Park, CA, 1991.Google Scholar
- 7.K. W. Kolence. Tile software empiricist. Performance Evaluation Review, 2(2):31 - 36, 1973. Google ScholarDigital Library
- 8.K. W. Kolence and P. J. Kiviat. Software unit profiles and Kiviat figures. Performance Evaluation Rewew, 2(3):2- 12, 1973. Google ScholarDigital Library
- 9.Heikki Mannila, Hannu Toivonen, and A. Inkeri Verkamo. Efficient algorithms for discovering association rules. In Usama M. Fayyad and Ramasamy Uthurusamy, editors, AAA1 Workshop on Knowledge Discovery in Databases, pages 181 - 192, Seattle, Washington, July 1994.Google Scholar
- 10.S. Moen. Drawing dynamic trees. IEEE Software, 7(4):21 - 28. July 1990. Google ScholarDigital Library
- 11.F. N. Paulisch and W. F. Tichy. EDGE: An extendible graph editor. Software--Practzce and Experience, 20(S1):63 - 88, June 1990. Google ScholarDigital Library
- 12.Gregory Piatetsky-Shapiro. Discovery, analysis, and presentation of strong rules. In Gregory Piatetsky- Shapiro and William 3. Frawley, editors, Knowledge Dzscovery ,n Databases, pages 229- 248. AAAI Press / The MIT Press, Menlo Park, CA. 1991.Google Scholar
- 13.Gregory Piatetsky-Shapiro and William J. Frawley, editors. Knowledge D,scovery in Databases. AAAI Press / The MIT Press, Menlo Park, CA, 1991. Google ScholarDigital Library
- 14.Gregory Piatetsky-Shapiro and Christopher J. Matheus. The interestingness of deviations. In Usarea M. Fayyad and Ramasamy Uthurusamy. editors, AAAI Workshop on Knowledge Discovery #n Databases. pages 25- 36, Seattle, Washington, July 1994.Google Scholar
- 15.L. A. Rowe, M. Davis, E. Messinger, C. Meyer, C. Spirakis, and A. Tuan. A browser for directed graphs. Software#Practice and Experience, 17(1):61 - 76, January 1987. Google ScholarDigital Library
- 16.J. Q. Walker II. A node-positioning algorithm for general trees. Software--Practzce and Expemence, 20(7):685 - 705, July 1990. Google ScholarDigital Library
Index Terms
- Finding interesting rules from large sets of discovered association rules
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