Mining frequent patterns by pattern-growth: methodology and implications

By Jiawei Han, Jian Pei. 2000

335 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

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

1. S. Kotsiantis
2. Dimitris N. Kanellopoulos
3. Cynthia Rudin
4. Octavio Loyola-González
5. Tong Wang
6. Berk Ustun
7. Lesia Semenova
8. Chudi Zhong

In Computer Networks and Communications

1. Cynthia Rudin
2. Allison Chang
3. Tianbao Yang
4. Robert E. Schapire
5. Yiming Ying
6. Boris G. Mirkin
7. Renato Cordeiro de Amorim
8. Yining Wang

In Information Systems

1. S. Kotsiantis
2. Dimitris N. Kanellopoulos
3. Cynthia Rudin
4. Margo I. Seltzer
5. Ş. Ertekin
6. Tong Wang
7. Alexey Ignatiev
8. Nicholas Larus-Stone

Citations in the corpus

• An optimization approach to learning falling rule lists (2017, methodology): “We used the frequent pattern growth (FP-growth) algorithm (Han and Pei, 2000) to generate the set of antecedents A from the dataset.”
• Data Mining for the Internet of Things: Literature Review and Challenges (2015, neutral): “The typical algorithm is FP-Growth algorithm [78].”
• Association Rules Mining: A Recent Overview (2006, background): “FP-Tree [15], frequent pattern mining, is another milestone in the development of association rule mining, which breaks the main bottlenecks of the Apriori.”