Discovering Surprising Patterns by Detecting Occurrences of Simpson’s Paradox

By Carem C. Fabris, Alex Alves Freitas. 2000

32 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, 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. Kristina Lerman
2. Nazanin Alipourfard
3. Peter G. Fennell
4. Alex Alves Freitas
5. Cynthia Rudin
6. Been Kim
7. Marco F. Huber
8. Finale Doshi-Velez

In Computational Theory and Mathematics

1. Kristina Lerman
2. Nazanin Alipourfard
3. Peter G. Fennell
4. Alex Alves Freitas

In Information Systems

1. Kristina Lerman
2. Peter G. Fennell
3. Nazanin Alipourfard
4. Alex Alves Freitas
5. Cynthia Rudin
6. Been Kim
7. Finale Doshi-Velez
8. Marco F. Huber

Citations in the corpus

• Using Simpson's Paradox to Discover Interesting Patterns in Behavioral Data (2018, methodology): “The idea for using Simpson’s paradox to uncover interesting patterns in data was explored by researchers in the past (Fabris and Freitas 2000).”
• Computational social scientist beware: Simpson’s paradox in behavioral data (2017, background): “Simpson’s paradox can indicate that interesting patterns exist in data [6], but it can also skew analysis.”
• Understanding the Crucial Role of Attribute Interaction in Data Mining (2001, methodology): “Fabris and Freitas (1999) have applied this algorithm to seven data sets of the UCI repository (http://www.”