Applying Objective Interestingness Measures in Data Mining Systems

By Robert J. Hilderman, Howard J. Hamilton. 2000

61 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. Yixin Chen
2. Zhicheng Cui
3. Fabrizio Silvestri
4. Andrew Haines
5. Mounia Lalmas
6. Dan Luo
7. Longbing Cao
8. Gabriele Tolomei

In Computational Theory and Mathematics

1. Dan Luo
2. Longbing Cao
3. Chengqi Zhang
4. Zhicheng Cui
5. Yixin Chen

In Information Systems

1. Yixin Chen
2. Fabrizio Silvestri
3. Dan Luo
4. Yujie He
5. Longbing Cao
6. Chengqi Zhang
7. Zhicheng Cui
8. Bernhard Schölkopf

Citations in the corpus

• Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking (2017, background): “Early works have focused on the development of interestingness metrics as proxy measures of knowledge actionability [3, 10].”
• Flexible Frameworks for Actionable Knowledge Discovery (2010, background): “Existing efforts in the development of effective interestingness metrics are basically on developing and refining objective technical interest-ingness metrics ( t o ðÞ ) [21], [25].”
• Optimal Action Extraction for Random Forests and Boosted Trees (2015, background): “Hil-derman et al. proposed an two-step process for ranking the interestingness of discovered patterns [17].”
• Knowledge actionability: satisfying technical and business interestingness (2007, background): “Moreover, business interestingness can also be instantiated into objective (Freitas, 1998; Hilderman and Hamilton, 2000) and subjective (Liu et al.”