Generating Synthetic Data to Match Data Mining Patterns

By J. Eno, C. Thompson. 2008

62 citations recorded by the source corpus.

Subfields: Artificial Intelligence, 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. Xin He
2. Samuel A. Assefa
3. Kaiyong Zhao
4. Xiaowen Chu
5. Francisco Cantú
6. Sebastián Saiegh
7. Marco F. Huber
8. Marc-André Zöller

In Information Systems

1. Xin He
2. Samuel A. Assefa
3. Kaiyong Zhao
4. Xiaowen Chu
5. Francisco Cantú
6. Sebastián Saiegh

Citations in the corpus

• AutoML: A Survey of the State-of-the-Art (2019, background): “[49] develop a synthetic data definition language (SDDL) to create new data for Iris dataset [50].”
• Fraudulent Democracy? An Analysis of Argentina's Infamous Decade Using Supervised Machine Learning (2011, background): “One alternative in overcoming these problems is to create synthetic data (Clifford and Heath 1993; Rubin 1993; Katz and Sala 1996; Lundin, Kvarnstr¨om, and Jonsson 2002; Reiter 2004; Eno and Thompson 2008).”
• Generating synthetic data in finance: opportunities, challenges and pitfalls (2020, background): “On the second point, Eno and Thompson [18] define a synthetic data language for tabular data, which is is a promising direction although lacking any privacy guarantees.”