Binary Rule Generation via Hamming Clustering

By M. Muselli, Diego Liberati. 2002

64 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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. Dennis Wei
2. Sanjeeb Dash
3. Dmitry M. Malioutov
4. Guolong Su
5. Oktay Günlük
6. Kush R. Varshney
7. Cynthia Rudin
8. Marco F. Huber

In Computational Theory and Mathematics

1. Dmitry M. Malioutov
2. Sanjeeb Dash
3. Kush R. Varshney
4. Oktay Günlük
5. Dennis Wei
6. Guolong Su

Citations in the corpus

• Interpretable Two-level Boolean Rule Learning for Classification (2015, methodology): “Another algorithm for DNF learning is the Hamming Clustering (HC) approach [13], which uses greedy methods to iteratively cluster samples in the same category and with features close to each other in Hamming distance.”
• Boolean Decision Rules via Column Generation (2018, background): “see also the survey [30]) of learning rules one by one and removing “covered” examples, a bottomup strategy of combining more specific rules into more general ones [45, 22, 41], and associative classification in which association rule mining is followed by rule selection using various criteria [38, ”