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.

Keywords

Association rule learningComputer scienceVisualizationRowFormalism (music)TemplateSet (abstract data type)Data miningTheoretical computer scienceProgramming language

Affiliated Institutions

Related Publications

Complex heatmap visualization

Abstract Heatmap is a widely used statistical visualization method on matrix‐like data to reveal similar patterns shared by subsets of rows and columns. In the R programming lan...

2022 iMeta 1414 citations

Publication Info

Year
1994
Type
article
Pages
401-407
Citations
717
Access
Closed

External Links

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

717
OpenAlex

Cite This

Mika Klemettinen, Heikki Mannila, P. Ronkainen et al. (1994). Finding interesting rules from large sets of discovered association rules. , 401-407. https://doi.org/10.1145/191246.191314

Identifiers

DOI
10.1145/191246.191314