Efficient algorithms for mining high-utility itemsets in uncertain databases

@article{Lin2016EfficientAF,
  title={Efficient algorithms for mining high-utility itemsets in uncertain databases},
  author={Chun-Wei Lin and Wensheng Gan and Philippe Fournier-Viger and Tzung-Pei Hong and Vincent S. Tseng},
  journal={Knowl.-Based Syst.},
  year={2016},
  volume={96},
  pages={171-187}
}
High-utility itemset mining (HUIM) is a useful set of techniques for discovering patterns in transaction databases, which considers both quantity and profit of items. However, most algorithms for mining highutility itemsets (HUIs) assume that the information stored in databases is precise, i.e., that there is no uncertainty. But in many real-life applications, an item or itemset is not only present or absent in transactions but is also associated with an existence probability. This is… CONTINUE READING
Highly Cited
This paper has 23 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 13 extracted citations

Hp-Apriori: Horizontal parallel-apriori algorithm for frequent itemset mining from big data

2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)( • 2017
View 1 Excerpt

Mining high utility partial periodic pattern by GPA

2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) • 2017
View 1 Excerpt

References

Publications referenced by this paper.
Showing 1-10 of 34 references

Quest synthetic data generator

R. Agrawal, R. Srikant
Available: http://www. 1148 Almaden.ibm.com/cs/quest/syndata.html • 1994
View 6 Excerpts
Highly Influenced

Mining Frequent Itemsets from Uncertain Data

View 13 Excerpts
Highly Influenced

Efficient query evaluation on probabilistic databases

The VLDB Journal • 2004
View 4 Excerpts
Highly Influenced

Mining High Utility Itemsets

View 4 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…