Efficient mining of both positive and negative association rules

By Xindong Wu, Chengqi Zhang, Shichao Zhang. 2004

526 citations recorded by the source corpus.

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

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. Cynthia Rudin
2. Benjamin Letham
3. David Madigan
4. Dino Pedreschi
5. Franco Turini
6. Wai Lam
7. Filippo Menczer
8. Christopher C. Yang

In Information Systems

1. Cynthia Rudin
2. Salvatore Ruggieri
3. Benjamin Letham
4. Franco Turini
5. Dino Pedreschi
6. David Madigan
7. Wai Lam
8. Filippo Menczer

In Signal Processing

1. Wai Lam
2. Dino Pedreschi
3. Franco Turini
4. David Madigan
5. Filippo Menczer
6. Christopher C. Yang
7. Salvatore Ruggieri
8. Benjamin Letham

Citations in the corpus

• Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model (2015, methodology): “Other techniques could be used for mining antecedents with negation, such as “not diabetes” [Wu, Zhang and Zhang (2004)].”
• Profiling relational data: a survey (2015, unknown): “required [135].”
• Data mining for discrimination discovery (2010, background): “As a consequence of the improved expressive power of this language, the formal results of this paper directly extend to association and classi.cation rules over over hierarchies [Srikant and Agrawal 1995] and negated itemsets [Wu et al. 2004].”
• Association Rules Mining: A Recent Overview (2006, unknown): “Wu et al [40] derived a new algorithm for generating both positive and negative association rules.”
• Introduction to the special topic section on mining Web resources for enhancing information retrieval (2007, methodology): “In addition, algorithms for mining positive- and negative association rules ( Wu, Zhang, & Zhang, 2004 ) are helpful for identifying positive- and negative-path web FDs.”