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https://ceur-ws.org/Vol-1703/paper1.pdf
Invited Talk
Constraint Programming for Constrained
Clustering
Christel Vrain
Université d’Orléans
INSA Centre Val de Loire, LIFO EA 4022
45067 Orléans, France
christel.vrain@univ-orleans.fr
Abstract. Several works have shown the interest of declarative frame-
works, such as Constraint Programming, SAT, Integer Linear Program-
ming, for Data Mining [9,10,12,8,14,15]. Relying on Constraint Program-
ming (CP) has several advantages: first, its inherent declarativity allows
to easily model constraints on the Data Mining problem at hand, second,
CP has the ability either to enumerate all the solutions or to find an op-
timal solution, in case of an optimization problem. Moreover, it relies on
constraint propagation, which often allows to efficiently prune the search
space.
I will mainly focus on constrained clustering [16,7]: user constraints are
put on the solutions, thus allowing to get a clustering closer to the one
expected by the user.
After giving some backgrounds on CP, I will present the seminal work of
[9] on CP for itemset mining, its extension to k-pattern set mining under
constraints [13] and its application to conceptual clustering.
The talk will then mainly be focused on distance-based constrained clus-
tering. I will show how we have modeled this task in CP [1,4,3], the
difficulties we have had to face, the solutions we have developed [2,5].
The interest of relying on CP will be illustrated through several exten-
sions [4,6,11].
The work on distance-based constrained clustering in CP is a joint work
with T.B.H. Dao and K.C. Duong.
References
1. Thi-Bich-Hanh Dao, Kanh-Chuong Duong, and Christel Vrain. A Declarative
Framework for Constrained Clustering. In Proceedings of the European Confer-
ence on Machine Learning and Principles and Practice of Knowledge Discovery in
Databases, pages 419–434, 2013.
2. Thi-Bich-Hanh Dao, Khanh-Chuong Duong, and Christel Vrain. A Filtering Al-
gorithm for Constrained Clustering with Within-Cluster Sum of Dissimilarities
Criterion. In Proceedings of the 25th International Conference on Tools with Arti-
ficial Intelligence, pages 1060–1067, 2013.
3. Thi-Bich-Hanh Dao, Khanh-Chuong Duong, and Christel Vrain. Clustering sous
contraintes en ppc. Revue d’Intelligence Artificielle, 28(5):523–545, 2014.
4. Thi-Bich-Hanh Dao, Khanh-Chuong Duong, and Christel Vrain. Constrained clus-
tering by constraint programming. Artificial Intelligence Journal, 2015.
5. Thi-Bich-Hanh Dao, Khanh-Chuong Duong, and Christel Vrain. Constrained min-
imum sum of squares clustering by constraint programming. In Principles and
Practice of Constraint Programming - 21st International Conference, CP 2015,
Cork, Ireland, August 31 - September 4, 2015, Proceedings, pages 557–573, 2015.
6. Thi-Bich-Hanh Dao, Christel Vrain, Khanh-Chuong Duong, and Ian Davidson. A
framework for actionable clustering using constraint programming. In ECAI 2016 -
22nd European Conference on Artificial Intelligence, 29 August-2 September 2016,
The Hague, The Netherlands - Including Prestigious Applications of Artificial In-
telligence (PAIS 2016), pages 453–461, 2016.
7. Ian Davidson and S. S. Ravi. The Complexity of Non-hierarchical Clustering
with Instance and Cluster Level Constraints. Data Mining Knowledge Discovery,
14(1):25–61, 2007.
8. Ian Davidson, S. S. Ravi, and Leonid Shamis. A SAT-based Framework for Efficient
Constrained Clustering. In Proceedings of the 10th SIAM International Conference
on Data Mining, pages 94–105, 2010.
9. Luc De Raedt, Tias Guns, and Siegfried Nijssen. Constraint programming for
itemset mining. In Proceedings of the 14th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining, pages 204–212, 2008.
10. Luc De Raedt, Tias Guns, and Siegfried Nijssen. Constraint Programming for
Data Mining and Machine Learning. In Proc. of the 24th AAAI Conference on
Artificial Intelligence, 2010.
11. Tias Guns, Thi-Bich-Hanh Dao, Christel Vrain, and Khanh-Chuong Duong. Repet-
itive branch-and-bound using constraint programming for constrained minimum
sum-of-squares clustering. In ECAI 2016 - 22nd European Conference on Artificial
Intelligence, 29 August-2 September 2016, The Hague, The Netherlands - Includ-
ing Prestigious Applications of Artificial Intelligence (PAIS 2016), pages 462–470,
2016.
12. Tias Guns, Siegfried Nijssen, and Luc De Raedt. Itemset mining: A constraint
programming perspective. Artificial Intelligence, 175:1951–1983, 2011.
13. Tias Guns, Siegfried Nijssen, and Luc De Raedt. k-Pattern set mining under
constraints. IEEE Transactions on Knowledge and Data Engineering, 25(2):402–
418, 2013.
14. Marianne Mueller and Stefan Kramer. Integer Linear Programming Models for
Constrained Clustering. In Proceedings of the 13th International Conference on
Discovery Science, pages 159–173, 2010.
15. Jean-Philippe Métivier, Patrice Boizumault, Bruno Cremilleux, Medhi Khiari, and
Samir Loudni. A constraint-based language for declarative pattern discovery. In
Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference on,
pages 1112–1119, Dec 2011.
16. Kiri Wagstaff and Claire Cardie. Clustering with instance-level constraints. In Pro-
ceedings of the 17th International Conference on Machine Learning, pages 1103–
1110, 2000.