Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases

By Vincent Shin-Mu Tseng, Bai-En Shie, Cheng-Wei Wu, Philip S. Yu. IEEE Transactions on Knowledge and Data Engineering, 2013

630 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/37ee043890512ff76fc1ff5cc82d905d4fd094a7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Philippe Fournier-Viger
2. Jun-Feng Qu
3. Mengchi Liu
4. Vincent Shin-Mu Tseng
5. Cheng-Wei Wu
6. Souleymane Zida

In Information Systems

1. Philippe Fournier-Viger
2. Jun-Feng Qu
3. Mengchi Liu
4. Vincent Shin-Mu Tseng
5. Cheng-Wei Wu
6. Souleymane Zida

In Signal Processing

1. Philippe Fournier-Viger
2. Jun-Feng Qu
3. Mengchi Liu
4. Vincent Shin-Mu Tseng
5. Cheng-Wei Wu
6. Souleymane Zida

Citations in the corpus

• Mining high utility itemsets without candidate generation (2012, methodology): “It is because that each node in the prefix-trees used in UP-Growth+ holds more information than that in the prefix-trees used in UPGrowth [22].”
• FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning (2014, methodology): “HUIM has a wide range of applications such as website click stream analysis, cross-marketing in retail stores and biomedical applications [2, 7, 10].”