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Partition-Based Clustering Using Constraint Optimization

Bibliographic reference Grossi, Valerio ; Guns, Tias ; Monreale, Anna ; Nanni, Mirco ; Nijssen, Siegfried. Partition-Based Clustering Using Constraint Optimization. In: Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi, Data mining and constraint programming - Foundations of a Cross-Disciplinary Approach, Springer  2016, p. 282-299
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  1. Aggarwal, C.C., Reddy, C.K.: Data Clustering: Algorithms and Applications, 1st edn. Chapman & Hall/CRC, Boca Raton (2013)
  2. Babaki Behrouz, Guns Tias, Nijssen Siegfried, Constrained Clustering Using Column Generation, Integration of AI and OR Techniques in Constraint Programming (2014) ISBN:9783319070452 p.438-454, 10.1007/978-3-319-07046-9_31
  3. Basu, S., Davidson, I., Wagstaff, K., Clustering, C.: Advances in Algorithms, Theory, and Applications, 1st edn. Chapman & Hall/CRC, Boca Raton (2008)
  4. Berthold Michael R., Borgelt Christian, Höppner Frank, Klawonn Frank, Guide to Intelligent Data Analysis, ISBN:9781848822597, 10.1007/978-1-84882-260-3
  5. Coscia Michele, Giannotti Fosca, Pedreschi Dino, A classification for community discovery methods in complex networks, 10.1002/sam.10133
  6. Dao Thi-Bich-Hanh, Duong Khanh-Chuong, Vrain Christel, A Filtering Algorithm for Constrained Clustering with Within-Cluster Sum of Dissimilarities Criterion, 10.1109/ictai.2013.158
  7. Dao Thi-Bich-Hanh, Duong Khanh-Chuong, Vrain Christel, A Declarative Framework for Constrained Clustering, Machine Learning and Knowledge Discovery in Databases (2013) ISBN:9783642409936 p.419-434, 10.1007/978-3-642-40994-3_27
  8. Dao Thi-Bich-Hanh, Duong Khanh-Chuong, Vrain Christel, Constrained clustering by constraint programming, 10.1016/j.artint.2015.05.006
  9. Davidson Ian, Ravi S. S., The complexity of non-hierarchical clustering with instance and cluster level constraints, 10.1007/s10618-006-0053-7
  10. Davidson Ian, Ravi S. S., Clustering With Constraints: Feasibility Issues and thek-Means Algorithm, Proceedings of the 2005 SIAM International Conference on Data Mining (2005) ISBN:9780898715934 p.138-149, 10.1137/1.9781611972757.13
  11. du Merle O., Hansen P., Jaumard B., Mladenovic N., An Interior Point Algorithm for Minimum Sum-of-Squares Clustering, 10.1137/s1064827597328327
  12. Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U.M. (eds.) KDD, pp. 226–231. AAAI Press, Menlo Park (1996)
  13. Gonzalez Teofilo F., Clustering to minimize the maximum intercluster distance, 10.1016/0304-3975(85)90224-5
  14. Hansen,P., Aloise, D.: A survey on exact methods for minimum sum-of-squares clustering, pp. 1–2, January 2009.
  15. Jain A. K., Murty M. N., Flynn P. J., Data clustering: a review, 10.1145/331499.331504
  16. Mueller Marianne, Kramer Stefan, Integer Linear Programming Models for Constrained Clustering, Discovery Science (2010) ISBN:9783642161834 p.159-173, 10.1007/978-3-642-16184-1_12
  17. Raghavan Usha Nandini, Albert Réka, Kumara Soundar, Near linear time algorithm to detect community structures in large-scale networks, 10.1103/physreve.76.036106
  18. Wagstaff, K., Cardie,C.: Clustering with instance-level constraints. In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29–July 2 2000, pp. 1103–1110 (2000)