Choosing the Right Lens: Finding What is Interesting in Data Mining

By Liqiang Geng, Howard J. Hamilton. 2007

67 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. Cynthia Rudin
2. Octavio Loyola-González
3. Tyler H. McCormick
4. David Madigan
5. Benjamin Letham
6. Tong Wang
7. Margo I. Seltzer
8. Himabindu Lakkaraju

In Information Systems

1. Cynthia Rudin
2. Benjamin Letham
3. David Madigan
4. Octavio Loyola-González
5. Tyler H. McCormick
6. Margo I. Seltzer
7. Ş. Ertekin
8. Tong Wang

Citations in the corpus

• Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “The best-known quality measures for rules are support and confidence [121], [122].”
• Learning theory analysis for association rules and sequential event prediction (2013, background): “Many “interestingness” measures have been proposed in the literature (see Tan et al., 2002; Geng and Hamilton, 2007; McGarry, 2005).”
• A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction (2011, background): “At each encounter, the provider records time-stamped medical symptoms experienced since the previous encounter.”