Fast algorithms for frequent itemset mining using FP-trees
By Gösta Grahne, Jianfei Zhu. IEEE Transactions on Knowledge and Data Engineering, 2005
629 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems
View Paper: https://www.semanticscholar.org/paper/453e5258a2f0949934d0e2838a6c2ca546f34457
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computational Theory and Mathematics
- Byeong-Soo Jeong
- Chin-chen Chang
- Syed Khairuzzaman Tanbeer
- Yu-Chiang Li
- Chowdhury Farhan Ahmed
- Young-Koo Lee
- Jieh-Shan Yeh
- Jun-Feng Qu
In Information Systems
- Byeong-Soo Jeong
- Chin-chen Chang
- Syed Khairuzzaman Tanbeer
- Yu-Chiang Li
- Chowdhury Farhan Ahmed
- Young-Koo Lee
- Jieh-Shan Yeh
- Jun-Feng Qu
Citations in the corpus
- Isolated items discarding strategy for discovering high utility itemsets (2008, methodology): “Numerous efficient methods have been proposed to discover frequent itemsets, such as level-wise algorithms [2,3,7,8,12,30] and pattern-growth methods [1,15,16,21,26].”
- Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases (2009, background): “…Jeong, and Young-Koo Lee, Member, IEEE Abstract—Recently, high utility pattern (HUP) mining is one of the most important research issues in data mining due to its ability to consider the nonbinary frequency values of items in transactions and different profit values for every item.”