Generating an informative cover for association rules (2002) [12 citations — 0 self]
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by Laurentiu Cristofor, Dan Simovici
In Proceedings of International Conference on Data Mining
http://www.cs.umb.edu/~dsim/papersps/rc3.ps
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Abstract:
Mining association rules may generate a large numbers of rules making the results hard to analyze manually. Pasquier et al. have discussed the generation of GuiguesDuquenne--Luxenburger basis (GD-L basis). Using a similar approach, we introduce a new rule of inference and define the notion of association rules cover as a minimal set of rules that are non-redundant with respect to this new rule of inference. Our experimental results (obtained using both synthetic and real data sets) show that our covers are smaller than the GD-L basis and they are computed in time that is comparable to the classic Apriori algorithm for generating rules. 1
Citations
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| 279 | Efficiently mining long patterns from databases – Bayardo - 1998 |
| 82 | Pruning and summarizing the discovered associations – LIU, HSU, et al. - 1999 |
| 76 | Efficient mining of association rules using closed itemset lattices – Pasquier, Bastide, et al. - 1999 |
| 27 | Closed Set Based Discovery of Small Covers for Association Rules – Pasquier, Bastide, et al. - 1999 |
| 24 | Implications partielles dans un contexte – Luxenburger - 1991 |
| 11 | Relational Database Systems – Simovici, Tenney - 1995 |
| 2 | ARtool: Association rule mining algorithms and tools – Cristofor - 2002 |
