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Identifying and generating easy sets of constraints for clustering (2006)
| Venue: | In AAAI ’06: Proceedings, The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference |
| Citations: | 18 - 5 self |
BibTeX
@INPROCEEDINGS{Davidson06identifyingand,
author = {Ian Davidson and S. S. Ravi},
title = {Identifying and generating easy sets of constraints for clustering},
booktitle = {In AAAI ’06: Proceedings, The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference},
year = {2006}
}
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Abstract
Clustering under constraints is a recent innovation in the ar-tificial intelligence community that has yielded significant practical benefit. However, recent work has shown that for some negative forms of constraints the associated sub-problem of just finding a feasible clustering is NP-complete. These worst case results for the entire problem class say noth-ing of where and how prevalent easy problem instances are. In this work, we show that there are large pockets within these problem classes where clustering under constraints is easy and that using easy sets of constraints yields better empirical results. We then illustrate several sufficient conditions from graph theory to identify a priori where these easy problem instances are and present algorithms to create large and easy to satisfy constraint sets.
Keyphrases
easy set associated sub-problem entire problem class graph theory easy problem instance several sufficient condition significant practical benefit constraint yield case result empirical result feasible clustering negative form present algorithm prevalent easy problem instance ar-tificial intelligence community recent innovation large pocket recent work constraint set
