Mining relational patterns from multiple relational tables
作者： Maytal Saar Tsechansky , Nava Pliskin , Gadi Rabinowitz , Avi Porath
DOI: 10.1016/S0167-9236(99)00043-3
关键词:
摘要: Abstract In this paper, we present the concept of relational patterns and our approach to extract them from multiple tables. Relational are analogous frequent itemsets extracted by Apriori algorithm [R. Agrawal, H. Mannila, R. Srikant, Toivonen, A.I. Verkamo, Advances in Knowledge Discovery Data Mining, AAAI Press, 1995.] case a single table. However, for tables, capture co-occurrences attributes as well relationships between these attributes, which essential avoid information loss. We describe experiences test-bed implementation on real hospital's discharge abstract database. This process raised issues, were then implemented order enhance an analyst's ability explore while preventing high diversity abundance available data blurring subtle interest. Finally, evaluate usefulness context other possible domains.
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sci-hub.se 参考文章(16)
Heikki Mannila, A. Inkeri Verkamo, Hannu Toivonen, Efficient algorithms for discovering association rules knowledge discovery and data mining. pp. 181- 192 ,(1994)
Gregory Piatetsky-Shapiro, Dwight McNeill, Christopher J. Matheus, An application of KEFIR to the analysis of healthcare information knowledge discovery and data mining. pp. 441- 452 ,(1994)
Gediminas Adomavicius, Alexander Tuzhilin, Discovery of Actionable Patterns in Databases: The Action Hierarchy Approach Social Science Research Network. ,(1997)
Randy Kerber, ChiMerge: discretization of numeric attributes national conference on artificial intelligence. pp. 123- 128 ,(1992)
Heikki Mannila, A. Inkeri Verkamo, Ramakrishnan Srikant, Hannu Toivonen, Rakesh Agrawal, Fast discovery of association rules knowledge discovery and data mining. pp. 307- 328 ,(1996)
Shamkant B. Navathe, Edward Omiecinski, Ashoka Savasere, An Efficient Algorithm for Mining Association Rules in Large Databases very large data bases. pp. 432- 444 ,(1995)
C. Domshlak, A. Meisels, N. Liusternik, S. E. Shimony, D. Gershkovich, E. Glides, T. Rosen, FlexiMine - a flexible platform for KDD research and application construction knowledge discovery and data mining. pp. 184- 188 ,(1998)
Mika Klemettinen, Heikki Mannila, Pirjo Ronkainen, Hannu Toivonen, A. Inkeri Verkamo, Finding interesting rules from large sets of discovered association rules conference on information and knowledge management. pp. 401- 407 ,(1994) , 10.1145/191246.191314
Ramakrishnan Srikant, Rakesh Agrawal, Mining quantitative association rules in large relational tables international conference on management of data. ,vol. 25, pp. 1- 12 ,(1996) , 10.1145/233269.233311
R. J. Miller, Y. Yang, Association rules over interval data international conference on management of data. ,vol. 26, pp. 452- 461 ,(1997) , 10.1145/253260.253361