Constrained clustering by constraint programming
Constrained Clustering allows to make the clustering task more accurate by integrating user constraints, which can be instance-level or cluster-level constraints. Few works consider the integration of different kinds of constraints, they are usually based on declarative frameworks and they are often...
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| Vydané v: | Artificial intelligence Ročník 244; číslo 244; s. 70 - 94 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Amsterdam
Elsevier B.V
01.03.2017
Elsevier Science Ltd Elsevier |
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| ISSN: | 0004-3702, 1872-7921 |
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| Abstract | Constrained Clustering allows to make the clustering task more accurate by integrating user constraints, which can be instance-level or cluster-level constraints. Few works consider the integration of different kinds of constraints, they are usually based on declarative frameworks and they are often exact methods, which either enumerate all the solutions satisfying the user constraints, or find a global optimum when an optimization criterion is specified. In a previous work, we have proposed a model for Constrained Clustering based on a Constraint Programming framework. It is declarative, allowing a user to integrate user constraints and to choose an optimization criterion among several ones. In this article we present a new and substantially improved model for Constrained Clustering, still based on a Constraint Programming framework. It differs from our earlier model in the way partitions are represented by means of variables and constraints. It is also more flexible since the number of clusters does not need to be set beforehand; only a lower and an upper bound on the number of clusters have to be provided. In order to make the model-based approach more efficient, we propose new global optimization constraints with dedicated filtering algorithms. We show that such a framework can easily be embedded in a more general process and we illustrate this on the problem of finding the optimal Pareto front of a bi-criterion constrained clustering task. We compare our approach with existing exact approaches, based either on a branch-and-bound approach or on graph coloring on twelve datasets. Experiments show that the model outperforms exact approaches in most cases. |
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| AbstractList | Constrained Clustering allows to make the clustering task more accurate by integrating user constraints, which can be instance-level or cluster-level constraints. Few works consider the integration of different kinds of constraints, they are usually based on declarative frameworks and they are often exact methods, which either enumerate all the solutions satisfying the user constraints, or find a global optimum when an optimization criterion is specified. In a previous work, we have proposed a model for Constrained Clustering based on a Constraint Programming framework. It is declarative, allowing a user to integrate user constraints and to choose an optimization criterion among several ones. In this article we present a new and substantially improved model for Constrained Clustering, still based on a Constraint Programming framework. It differs from our earlier model in the way partitions are represented by means of variables and constraints. It is also more flexible since the number of clusters does not need to be set beforehand; only a lower and an upper bound on the number of clusters have to be provided. In order to make the model-based approach more efficient, we propose new global optimization constraints with dedicated filtering algorithms. We show that such a framework can easily be embedded in a more general process and we illustrate this on the problem of finding the optimal Pareto front of a bi-criterion constrained clustering task. We compare our approach with existing exact approaches, based either on a branch-and-bound approach or on graph coloring on twelve datasets. Experiments show that the model outperforms exact approaches in most cases. |
| Author | Vrain, Christel Dao, Thi-Bich-Hanh Duong, Khanh-Chuong |
| Author_xml | – sequence: 1 givenname: Thi-Bich-Hanh surname: Dao fullname: Dao, Thi-Bich-Hanh email: thi-bich-hanh.dao@univ-orleans.fr – sequence: 2 givenname: Khanh-Chuong surname: Duong fullname: Duong, Khanh-Chuong email: khanh-chuong.duong@univ-orleans.fr – sequence: 3 givenname: Christel surname: Vrain fullname: Vrain, Christel email: christel.vrain@univ-orleans.fr |
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| Cites_doi | 10.1109/TKDE.2011.204 10.2307/2344237 10.1007/s11222-007-9033-z 10.1002/1520-6750(199106)38:3<447::AID-NAV3220380312>3.0.CO;2-0 10.1007/BF01246100 10.1287/ijoc.8.4.344 10.1007/BF02289588 10.1080/01621459.1978.10481589 10.1007/s10618-006-0053-7 10.1007/s10107-010-0349-7 10.1016/j.artint.2011.05.002 10.1007/s10618-012-0291-9 10.1109/TPAMI.1980.4767027 10.1016/0304-3975(85)90224-5 |
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| Keywords | Filtering algorithm Global optimization constraint Bi-criterion clustering Modeling Constrained clustering Constraint programming |
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| References | Mueller, Kramer (br0020) 2010 Law, Lee (br0470) 2004 Focacci, Lodi, Milano (br0160) 1999 Berg, Jarvisalo (br0390) 2013 Gilpin, Davidson (br0380) 2011 Davidson, Ravi (br0250) 2005 Cormack (br0100) 1971; 134 Davidson, Ravi (br0200) 2007; 14 Brusco, Stahl (br0040) 2005 Guns, Nijssen, De Raedt (br0280) 2013; 25 Rojas, Boizumault, Loudni, Crémilleux, Lepailleur (br0450) 2014 Cambazard, Hadzic, O'Sullivan (br0420) 2010 De Raedt, Guns, Nijssen (br0400) 2008 Lu, Carreira-Perpinan (br0260) 2008 K. Bache, M. Lichman, UCI machine learning repository (br0180) 2006 Kotthoff, O'Sullivan (br0320) 2013 Wang, Yan, Sriskandarajah (br0120) 1996; 13 Luxburg (br0330) 2007; 17 Davidson, Ravi, Shamis (br0010) 2010 Beldiceanu, Carlsson, Rampon (br0140) Dao, Duong, Vrain (br0030) 2013 Gilpin, Nijssen, Davidson (br0370) 2013 . Delattre, Hansen (br0050) 1980; 4 Aloise, Hansen, Liberti (br0310) 2012; 131 Bessiere, Hebrard, O'Sullivan (br0460) 2009 Régin (br0150) 1994 Régin (br0170) 1999 T'kindt, Billaut (br0500) 2006 Hansen, Delattre (br0210) 1978; 73 Jabbour, Sais, Salhi (br0440) 2013 Klein, Aronson (br0490) 1991; 38 Davidson, Ravi (br0080) 2005 Basu, Davidson, Wagstaff (br0220) 2008 Guns, Nijssen, De Raedt (br0430) 2011; 175 br0190 De Raedt, Guns, Nijssen (br0410) 2010 Wagstaff, Cardie, Rogers, Schrödl (br0230) 2001 Bilenko, Basu, Mooney (br0240) 2004 Dao, Duong, Vrain (br0480) 2013 Wang, Chen (br0130) 2012 Johnson (br0110) 1967; 32 Schaus, Hartert (br0520) 2013 Mehrotra, Trick (br0540) 1995; 8 Wagstaff, Cardie (br0070) 2000 Métivier, Boizumault, Crémilleux, Khiari, Loudni (br0290) 2012 Davidson, Qian, Wang, Ye (br0360) 2013 Wang, Davidson (br0270) 2010 Gavanelli (br0510) 2002 Babaki, Guns, Nijssen (br0300) 2014 Zhi, Wang, Qian, Butler, Ramakrishnan, Davidson (br0350) 2013 Wang, Qian, Davidson (br0340) 2014; 28 Gonzalez (br0060) 1985; 38 Ester, Kriegel, Sander, Xu (br0090) 1996 Delattre (10.1016/j.artint.2015.05.006_br0050) 1980; 4 Lu (10.1016/j.artint.2015.05.006_br0260) 2008 Wang (10.1016/j.artint.2015.05.006_br0340) 2014; 28 Aloise (10.1016/j.artint.2015.05.006_br0310) 2012; 131 Brusco (10.1016/j.artint.2015.05.006_br0040) 2005 Johnson (10.1016/j.artint.2015.05.006_br0110) 1967; 32 Law (10.1016/j.artint.2015.05.006_br0470) 2004 Mehrotra (10.1016/j.artint.2015.05.006_br0540) 1995; 8 Wagstaff (10.1016/j.artint.2015.05.006_br0070) 2000 Wang (10.1016/j.artint.2015.05.006_br0120) 1996; 13 Wang (10.1016/j.artint.2015.05.006_br0130) 2012 Guns (10.1016/j.artint.2015.05.006_br0280) 2013; 25 Davidson (10.1016/j.artint.2015.05.006_br0360) 2013 Wang (10.1016/j.artint.2015.05.006_br0270) 2010 Guns (10.1016/j.artint.2015.05.006_br0430) 2011; 175 Mueller (10.1016/j.artint.2015.05.006_br0020) 2010 Focacci (10.1016/j.artint.2015.05.006_br0160) 1999 Davidson (10.1016/j.artint.2015.05.006_br0200) 2007; 14 (10.1016/j.artint.2015.05.006_br0180) 2006 Rojas (10.1016/j.artint.2015.05.006_br0450) 2014 Basu (10.1016/j.artint.2015.05.006_br0220) 2008 Jabbour (10.1016/j.artint.2015.05.006_br0440) 2013 Wagstaff (10.1016/j.artint.2015.05.006_br0230) 2001 Gilpin (10.1016/j.artint.2015.05.006_br0380) 2011 Cambazard (10.1016/j.artint.2015.05.006_br0420) 2010 Klein (10.1016/j.artint.2015.05.006_br0490) 1991; 38 Davidson (10.1016/j.artint.2015.05.006_br0080) 2005 T'kindt (10.1016/j.artint.2015.05.006_br0500) 2006 Régin (10.1016/j.artint.2015.05.006_br0170) 1999 Régin (10.1016/j.artint.2015.05.006_br0150) 1994 Bilenko (10.1016/j.artint.2015.05.006_br0240) 2004 Beldiceanu (10.1016/j.artint.2015.05.006_br0140) Ester (10.1016/j.artint.2015.05.006_br0090) 1996 Dao (10.1016/j.artint.2015.05.006_br0480) 2013 Davidson (10.1016/j.artint.2015.05.006_br0010) 2010 Kotthoff (10.1016/j.artint.2015.05.006_br0320) 2013 Luxburg (10.1016/j.artint.2015.05.006_br0330) 2007; 17 Hansen (10.1016/j.artint.2015.05.006_br0210) 1978; 73 Babaki (10.1016/j.artint.2015.05.006_br0300) 2014 Schaus (10.1016/j.artint.2015.05.006_br0520) 2013 De Raedt (10.1016/j.artint.2015.05.006_br0410) 2010 Cormack (10.1016/j.artint.2015.05.006_br0100) 1971; 134 Zhi (10.1016/j.artint.2015.05.006_br0350) 2013 Métivier (10.1016/j.artint.2015.05.006_br0290) 2012 10.1016/j.artint.2015.05.006_br0530 Dao (10.1016/j.artint.2015.05.006_br0030) 2013 Berg (10.1016/j.artint.2015.05.006_br0390) 2013 Bessiere (10.1016/j.artint.2015.05.006_br0460) 2009 De Raedt (10.1016/j.artint.2015.05.006_br0400) 2008 Gonzalez (10.1016/j.artint.2015.05.006_br0060) 1985; 38 Gilpin (10.1016/j.artint.2015.05.006_br0370) 2013 Davidson (10.1016/j.artint.2015.05.006_br0250) 2005 Gavanelli (10.1016/j.artint.2015.05.006_br0510) 2002 |
| References_xml | – year: 2006 ident: br0500 article-title: Multicriteria Scheduling, Theory, Models and Algorithms – start-page: 173 year: 2009 end-page: 187 ident: br0460 article-title: Minimising decision tree size as combinatorial optimisation publication-title: Proceedings of the 15th International Conference on Principles and Practice of Constraint Programming – start-page: 59 year: 2005 end-page: 70 ident: br0250 article-title: Agglomerative hierarchical clustering with constraints: theoretical and empirical results publication-title: Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases – start-page: 138 year: 2005 end-page: 149 ident: br0080 article-title: Clustering with constraints: feasibility issues and the k-means algorithm publication-title: Proceedings of the 5th SIAM International Conference on Data Mining – start-page: 390 year: 1999 end-page: 404 ident: br0170 article-title: Arc consistency for global cardinality constraints with costs publication-title: Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming – start-page: 362 year: 2004 end-page: 376 ident: br0470 article-title: Global constraints for integer and set value precedence publication-title: Proceedings of the 10th International Conference on Principles and Practice of Constraint Programming – start-page: 611 year: 2013 end-page: 627 ident: br0520 article-title: Multi-objective large neighborhood search publication-title: Proceedings of the 19th International Conference on Principles and Practice of Constraint Programming – volume: 32 start-page: 241 year: 1967 end-page: 254 ident: br0110 article-title: Hierarchical clustering schemes publication-title: Psychometrika – start-page: 11 year: 2004 end-page: 18 ident: br0240 article-title: Integrating constraints and metric learning in semi-supervised clustering publication-title: Proceedings of the 21st International Conference on Machine Learning – volume: 4 start-page: 277 year: 1980 end-page: 291 ident: br0050 article-title: Bicriterion cluster analysis publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – start-page: 204 year: 2008 end-page: 212 ident: br0400 article-title: Constraint programming for itemset mining publication-title: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – start-page: 372 year: 2013 end-page: 378 ident: br0370 article-title: Formalizing hierarchical clustering as integer linear programming publication-title: Proceedings of the 27th AAAI Conference on Artificial Intelligence – start-page: 750 year: 2013 end-page: 757 ident: br0390 article-title: Optimal correlation clustering via MaxSAT publication-title: Proceedings of the 13th IEEE International Conference on Data Mining Workshops – start-page: 563 year: 2010 end-page: 572 ident: br0270 article-title: Flexible constrained spectral clustering publication-title: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – volume: 73 start-page: 397 year: 1978 end-page: 403 ident: br0210 article-title: Complete-link cluster analysis by graph coloring publication-title: J. Am. Stat. Assoc. – start-page: 159 year: 2010 end-page: 173 ident: br0020 article-title: Integer linear programming models for constrained clustering publication-title: Proceedings of the 13th International Conference on Discovery Science – start-page: 577 year: 2001 end-page: 584 ident: br0230 article-title: Constrained k-means clustering with background knowledge publication-title: Proceedings of the 18th International Conference on Machine Learning – start-page: 419 year: 2013 end-page: 434 ident: br0030 article-title: A declarative framework for constrained clustering publication-title: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases – start-page: 136 year: 2002 end-page: 140 ident: br0510 article-title: An algorithm for multi-criteria optimization in CSPs publication-title: Proceedings of the 15th European Conference on Artificial Intelligence – start-page: 207 year: 2012 end-page: 218 ident: br0290 article-title: Constrained clustering using SAT publication-title: Proceedings of the 11th International Symposium on Advances in Intelligent Data Analysis – year: 2010 ident: br0410 article-title: Constraint programming for data mining and machine learning publication-title: Proc. of the 24th AAAI Conference on Artificial Intelligence – year: 2006 ident: br0180 publication-title: Handbook of Constraint Programming, Foundations of Artificial Intelligence – ident: br0190 – volume: 14 start-page: 25 year: 2007 end-page: 61 ident: br0200 article-title: The complexity of non-hierarchical clustering with instance and cluster level constraints publication-title: Data Min. Knowl. Discov. – volume: 131 start-page: 195 year: 2012 end-page: 220 ident: br0310 article-title: An improved column generation algorithm for minimum sum-of-squares clustering publication-title: Math. Program. – volume: 28 start-page: 1 year: 2014 end-page: 30 ident: br0340 article-title: On constrained spectral clustering and its applications publication-title: Data Min. Knowl. Discov. – year: 2008 ident: br0220 article-title: Constrained Clustering: Advances in Algorithms, Theory, and Applications – year: 2005 ident: br0040 article-title: Branch-and-Bound Applications in Combinatorial Data Analysis (Statistics and Computing) – volume: 134 start-page: 321 year: 1971 end-page: 367 ident: br0100 article-title: A review of classification publication-title: J. R. Stat. Soc. A – ident: br0140 article-title: Global constraint catalog – volume: 17 start-page: 395 year: 2007 end-page: 416 ident: br0330 article-title: A tutorial on spectral clustering publication-title: Stat. Comput. – volume: 8 start-page: 344 year: 1995 end-page: 354 ident: br0540 article-title: A column generation approach for graph coloring publication-title: INFORMS J. Comput. – year: 2012 ident: br0130 article-title: Clustering to maximize the ratio of split to diameter publication-title: Proceedings of the 29th International Conference on Machine Learning – reference: K. Bache, M. Lichman, UCI machine learning repository, – volume: 13 start-page: 231 year: 1996 end-page: 248 ident: br0120 article-title: The weighted sum of split and diameter clustering publication-title: J. Classif. – start-page: 94 year: 2010 end-page: 105 ident: br0010 article-title: A SAT-based framework for efficient constrained clustering publication-title: Proceedings of the 10th SIAM International Conference on Data Mining – start-page: 1136 year: 2011 end-page: 1144 ident: br0380 article-title: Incorporating SAT solvers into hierarchical clustering algorithms: an efficient and flexible approach publication-title: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – year: 2013 ident: br0350 article-title: Clustering with complex constraints – algorithms and applications publication-title: Proceedings of the 27th AAAI Conference on Artificial Intelligence – start-page: 1103 year: 2000 end-page: 1110 ident: br0070 article-title: Clustering with instance-level constraints publication-title: Proceedings of the 17th International Conference on Machine Learning – volume: 38 start-page: 447 year: 1991 end-page: 461 ident: br0490 article-title: Optimal clustering: a model and method publication-title: Nav. Res. Logist. – start-page: 403 year: 2013 end-page: 418 ident: br0440 article-title: The top-k frequent closed itemset mining using top-k SAT problem publication-title: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases – start-page: 226 year: 1996 end-page: 231 ident: br0090 article-title: A density-based algorithm for discovering clusters in large spatial databases with noise publication-title: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining – volume: 25 start-page: 402 year: 2013 end-page: 418 ident: br0280 article-title: k-Pattern set mining under constraints publication-title: IEEE Trans. Knowl. Data Eng. – start-page: 234 year: 2013 end-page: 242 ident: br0360 article-title: Multi-objective multi-view spectral clustering via Pareto optimization publication-title: Proceedings of the 13th SIAM International Conference on Data Mining – volume: 38 start-page: 293 year: 1985 end-page: 306 ident: br0060 article-title: Clustering to minimize the maximum intercluster distance publication-title: Theor. Comput. Sci. – start-page: 438 year: 2014 end-page: 454 ident: br0300 article-title: Constrained clustering using column generation publication-title: Proceedings of the 11th International Conference on Integration of AI and oR Techniques in Constraint Programming for Combinatorial Optimization Problems – year: 2013 ident: br0320 article-title: Constraint-based clustering publication-title: Proceedings of the 10th International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) Techniques in Constraint Programming – start-page: 362 year: 1994 end-page: 367 ident: br0150 article-title: A filtering algorithm for constraints of difference in CSPs publication-title: Proceedings of the 12th National Conference on Artificial Intelligence, Vol. 1 – start-page: 1060 year: 2013 end-page: 1067 ident: br0480 article-title: A filtering algorithm for constrained clustering with within-cluster sum of dissimilarities criterion publication-title: Proceedings of the 25th International Conference on Tools with Artificial Intelligence – start-page: 1109 year: 2010 end-page: 1110 ident: br0420 article-title: Knowledge compilation for itemset mining publication-title: Proceedings of the 19th European Conference on Artificial Intelligence – reference: . – start-page: 189 year: 1999 end-page: 203 ident: br0160 article-title: Cost-based domain filtering publication-title: Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming – start-page: 71 year: 2014 end-page: 87 ident: br0450 article-title: Mining (soft-) skypatterns using dynamic CSP publication-title: Proceedings of the 11th International Conference on Integration of AI and OR Techniques in Constraint Programming – start-page: 1 year: 2008 end-page: 8 ident: br0260 article-title: Constrained spectral clustering through affinity propagation publication-title: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition – volume: 175 start-page: 1951 year: 2011 end-page: 1983 ident: br0430 article-title: Itemset mining: a constraint programming perspective publication-title: Artif. Intell. – start-page: 138 year: 2005 ident: 10.1016/j.artint.2015.05.006_br0080 article-title: Clustering with constraints: feasibility issues and the k-means algorithm – start-page: 94 year: 2010 ident: 10.1016/j.artint.2015.05.006_br0010 article-title: A SAT-based framework for efficient constrained clustering – volume: 25 start-page: 402 issue: 2 year: 2013 ident: 10.1016/j.artint.2015.05.006_br0280 article-title: k-Pattern set mining under constraints publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2011.204 – start-page: 750 year: 2013 ident: 10.1016/j.artint.2015.05.006_br0390 article-title: Optimal correlation clustering via MaxSAT – start-page: 419 year: 2013 ident: 10.1016/j.artint.2015.05.006_br0030 article-title: A declarative framework for constrained clustering – start-page: 207 year: 2012 ident: 10.1016/j.artint.2015.05.006_br0290 article-title: Constrained clustering using SAT – start-page: 204 year: 2008 ident: 10.1016/j.artint.2015.05.006_br0400 article-title: Constraint programming for itemset mining – year: 2013 ident: 10.1016/j.artint.2015.05.006_br0350 article-title: Clustering with complex constraints – algorithms and applications – start-page: 372 year: 2013 ident: 10.1016/j.artint.2015.05.006_br0370 article-title: Formalizing hierarchical clustering as integer linear programming – start-page: 1 year: 2008 ident: 10.1016/j.artint.2015.05.006_br0260 article-title: Constrained spectral clustering through affinity propagation – start-page: 362 year: 1994 ident: 10.1016/j.artint.2015.05.006_br0150 article-title: A filtering algorithm for constraints of difference in CSPs – start-page: 71 year: 2014 ident: 10.1016/j.artint.2015.05.006_br0450 article-title: Mining (soft-) skypatterns using dynamic CSP – start-page: 1136 year: 2011 ident: 10.1016/j.artint.2015.05.006_br0380 article-title: Incorporating SAT solvers into hierarchical clustering algorithms: an efficient and flexible approach – volume: 134 start-page: 321 issue: 3 year: 1971 ident: 10.1016/j.artint.2015.05.006_br0100 article-title: A review of classification publication-title: J. R. Stat. Soc. A doi: 10.2307/2344237 – year: 2008 ident: 10.1016/j.artint.2015.05.006_br0220 – year: 2012 ident: 10.1016/j.artint.2015.05.006_br0130 article-title: Clustering to maximize the ratio of split to diameter – volume: 17 start-page: 395 issue: 4 year: 2007 ident: 10.1016/j.artint.2015.05.006_br0330 article-title: A tutorial on spectral clustering publication-title: Stat. Comput. doi: 10.1007/s11222-007-9033-z – start-page: 563 year: 2010 ident: 10.1016/j.artint.2015.05.006_br0270 article-title: Flexible constrained spectral clustering – volume: 38 start-page: 447 issue: 3 year: 1991 ident: 10.1016/j.artint.2015.05.006_br0490 article-title: Optimal clustering: a model and method publication-title: Nav. Res. Logist. doi: 10.1002/1520-6750(199106)38:3<447::AID-NAV3220380312>3.0.CO;2-0 – start-page: 226 year: 1996 ident: 10.1016/j.artint.2015.05.006_br0090 article-title: A density-based algorithm for discovering clusters in large spatial databases with noise – start-page: 438 year: 2014 ident: 10.1016/j.artint.2015.05.006_br0300 article-title: Constrained clustering using column generation – volume: 13 start-page: 231 issue: 2 year: 1996 ident: 10.1016/j.artint.2015.05.006_br0120 article-title: The weighted sum of split and diameter clustering publication-title: J. Classif. doi: 10.1007/BF01246100 – start-page: 577 year: 2001 ident: 10.1016/j.artint.2015.05.006_br0230 article-title: Constrained k-means clustering with background knowledge – volume: 8 start-page: 344 year: 1995 ident: 10.1016/j.artint.2015.05.006_br0540 article-title: A column generation approach for graph coloring publication-title: INFORMS J. Comput. doi: 10.1287/ijoc.8.4.344 – volume: 32 start-page: 241 issue: 3 year: 1967 ident: 10.1016/j.artint.2015.05.006_br0110 article-title: Hierarchical clustering schemes publication-title: Psychometrika doi: 10.1007/BF02289588 – ident: 10.1016/j.artint.2015.05.006_br0140 – start-page: 11 year: 2004 ident: 10.1016/j.artint.2015.05.006_br0240 article-title: Integrating constraints and metric learning in semi-supervised clustering – year: 2010 ident: 10.1016/j.artint.2015.05.006_br0410 article-title: Constraint programming for data mining and machine learning – start-page: 136 year: 2002 ident: 10.1016/j.artint.2015.05.006_br0510 article-title: An algorithm for multi-criteria optimization in CSPs – start-page: 173 year: 2009 ident: 10.1016/j.artint.2015.05.006_br0460 article-title: Minimising decision tree size as combinatorial optimisation – start-page: 390 year: 1999 ident: 10.1016/j.artint.2015.05.006_br0170 article-title: Arc consistency for global cardinality constraints with costs – volume: 73 start-page: 397 issue: 362 year: 1978 ident: 10.1016/j.artint.2015.05.006_br0210 article-title: Complete-link cluster analysis by graph coloring publication-title: J. Am. Stat. Assoc. doi: 10.1080/01621459.1978.10481589 – start-page: 1109 year: 2010 ident: 10.1016/j.artint.2015.05.006_br0420 article-title: Knowledge compilation for itemset mining – start-page: 159 year: 2010 ident: 10.1016/j.artint.2015.05.006_br0020 article-title: Integer linear programming models for constrained clustering – year: 2005 ident: 10.1016/j.artint.2015.05.006_br0040 – year: 2006 ident: 10.1016/j.artint.2015.05.006_br0500 – start-page: 234 year: 2013 ident: 10.1016/j.artint.2015.05.006_br0360 article-title: Multi-objective multi-view spectral clustering via Pareto optimization – year: 2013 ident: 10.1016/j.artint.2015.05.006_br0320 article-title: Constraint-based clustering – volume: 14 start-page: 25 issue: 1 year: 2007 ident: 10.1016/j.artint.2015.05.006_br0200 article-title: The complexity of non-hierarchical clustering with instance and cluster level constraints publication-title: Data Min. Knowl. Discov. doi: 10.1007/s10618-006-0053-7 – start-page: 611 year: 2013 ident: 10.1016/j.artint.2015.05.006_br0520 article-title: Multi-objective large neighborhood search – volume: 131 start-page: 195 issue: 1–2 year: 2012 ident: 10.1016/j.artint.2015.05.006_br0310 article-title: An improved column generation algorithm for minimum sum-of-squares clustering publication-title: Math. Program. doi: 10.1007/s10107-010-0349-7 – ident: 10.1016/j.artint.2015.05.006_br0530 – start-page: 1103 year: 2000 ident: 10.1016/j.artint.2015.05.006_br0070 article-title: Clustering with instance-level constraints – volume: 175 start-page: 1951 year: 2011 ident: 10.1016/j.artint.2015.05.006_br0430 article-title: Itemset mining: a constraint programming perspective publication-title: Artif. Intell. doi: 10.1016/j.artint.2011.05.002 – start-page: 403 year: 2013 ident: 10.1016/j.artint.2015.05.006_br0440 article-title: The top-k frequent closed itemset mining using top-k SAT problem – start-page: 1060 year: 2013 ident: 10.1016/j.artint.2015.05.006_br0480 article-title: A filtering algorithm for constrained clustering with within-cluster sum of dissimilarities criterion – year: 2006 ident: 10.1016/j.artint.2015.05.006_br0180 – start-page: 189 year: 1999 ident: 10.1016/j.artint.2015.05.006_br0160 article-title: Cost-based domain filtering – volume: 28 start-page: 1 issue: 1 year: 2014 ident: 10.1016/j.artint.2015.05.006_br0340 article-title: On constrained spectral clustering and its applications publication-title: Data Min. Knowl. Discov. doi: 10.1007/s10618-012-0291-9 – volume: 4 start-page: 277 year: 1980 ident: 10.1016/j.artint.2015.05.006_br0050 article-title: Bicriterion cluster analysis publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.1980.4767027 – start-page: 362 year: 2004 ident: 10.1016/j.artint.2015.05.006_br0470 article-title: Global constraints for integer and set value precedence – volume: 38 start-page: 293 year: 1985 ident: 10.1016/j.artint.2015.05.006_br0060 article-title: Clustering to minimize the maximum intercluster distance publication-title: Theor. Comput. Sci. doi: 10.1016/0304-3975(85)90224-5 – start-page: 59 year: 2005 ident: 10.1016/j.artint.2015.05.006_br0250 article-title: Agglomerative hierarchical clustering with constraints: theoretical and empirical results |
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| SubjectTerms | Artificial Intelligence Bi-criterion clustering Clustering Clusters Coloring Computer Science Constrained clustering Constraint programming Constraints Criteria Filtering algorithm Filtration Global optimization Global optimization constraint Graph coloring Modeling Partitions Programming Theory of constraints |
| Title | Constrained clustering by constraint programming |
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