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Bibliography

Major publications by the team in recent years
  • 1C. Bach, D. L. Scapin.

    Comparing Inspections and User Testing for the Evaluation of Virtual Environments, in: International Journal of Human-Computer Interaction, July 2010, vol. 26, no 8, pp. 786-824. [ DOI : 10.1080/10447318.2010.487195 ]

    http://hal.inria.fr/hal-00950036
  • 2S. Caffiau, D. Scapin, P. Girard, M. Baron, F. Jambon.

    Increasing the expressive power of task analysis: Systematic comparison and empirical assessment of tool-supported task models, in: Interacting with Computers, November 2010, vol. 22, no 6, pp. 569-593. [ DOI : 10.1016/j.intcom.2010.06.003 ]

    http://hal.inria.fr/hal-00950015
  • 3S. Chelcea, P. Bertrand, B. Trousse.

    Un Nouvel Algorithme de Classification Ascendante 2-3 Hiérarchique, in: Actes de 14ème Congrès Francophone AFRIF-AFIA de Reconnaissance des Formes et Intelligence Artificielle (RFIA 2004), Centre de Congrès Pierre BAUDIS, Toulouse, France, 28-30 Janvier 2004, vol. 3, pp. 1471-1480.
  • 4A. Da Silva, Y. Lechevallier, R. Seraoui.

    A Clustering Approach to Monitor System Working, in: Learning and Data Science, M. Gettler Summa, L. Bottou, B. Goldfarb, F. Murtagh, C. Pardoux, M. Touati (editors), CRC Computer Science & Data Analysis, Chapman & Hall, 2011, vol. K13059, chap. 10.
  • 5G. Hébrail, B. Hugueney, Y. Lechevallier, F. Rossi.

    Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation, in: Neurocomputing / EEG Neurocomputing, Mar 2010, vol. 73, no 7-9, pp. 1125-1141.

    http://hal.inria.fr/hal-00515908/en
  • 6A. Marascu, F. Masseglia.

    Atypicity Detection in Data Streams: a Self-Adjusting Approach, in: Intelligent Data Analysis, January 2011, vol. 15, no 1, pp. 89-105. [ DOI : 10.3233/IDA-2010-0457 ]

    http://hal.inria.fr/hal-00789034
  • 7F. Masseglia, P. Poncelet, M. Teisseire, A. Marascu.

    Web Usage Mining: Extracting Unexpected Periods from Web Logs, in: Data Mining and Knowledge Discovery, February 2008, vol. 16, no 1, pp. 039-065. [ DOI : 10.1007/s10618-007-0080-z ]

    http://hal-lirmm.ccsd.cnrs.fr/lirmm-00204872
  • 8F. Masseglia, D. Tanasa, B. Trousse.

    Web Usage Mining: Sequential Pattern Extraction with a Very Low Support, in: Advanced Web Technologies and Applications: 6th Asia-Pacific Web Conference, APWeb 2004, Hangzhou, Chine, LNCS 3007, Springer Verlag, April 2004, pp. 513–522.

    http://hal.inria.fr/hal-00950768
  • 9M. Pallot, B. Trousse, B. Senach, D. L. Scapin.

    Living Lab Research Landscape: From User Centred Design and User Experience towards User Cocreation, in: First European Summer School "Living Labs", Paris, France, Inria (ICT Usage Lab), Userlab, EsoceNet, Universcience, August 2010.

    http://hal.inria.fr/inria-00612632
  • 10F. Rossi, B. Conan-Guez.

    Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis, in: Neural Networks, 2005, vol. 18, no 1, pp. 45–60. [ DOI : 10.1016/j.neunet.2004.07.001 ]

    http://hal.inria.fr/inria-00000599
  • 11D. L. Scapin, B. Senach, B. Trousse, M. Pallot.

    User Experience: Buzzword or New Paradigm?, in: ACHI 2012, The Fifth International Conference on Advances in Computer-Human Interactions, Valencia, Espagne, 2012.

    http://hal.inria.fr/hal-00769619
  • 12H. Schaffers, N. Komninos, M. Pallot, M. Aguas, E. Almirall, T. Bakici, J. Barroca, D. Carter, M. Corriou, J. Fernadez, H. Hielkema, A. Kivilehto, M. Nilsson, A. Oliveira, E. Posio, A. Sällström, R. Santoro, B. Senach, I. Torres, P. Tsarchopoulos, B. Trousse, P. Turkama, J. Lopez Ventura.

    FIREBALL white paper on Smart Cities as Innovation Ecosystems sustained by the Future Internet, 2012.

    http://hal.inria.fr/hal-00769635
  • 13H. Schaffers, N. Komninos, M. Pallot, B. Trousse, M. Nilsson, A. Oliveira.

    Smart Cities and the Future Internet: Towards Cooperation Frameworks for Open Innovation, in: Future Internet Assembly, J. Domingue, et al. (editors), LNCS 6656, Springer Verlag, May 2011, pp. 431-446. [ DOI : 10.1007/978-3-642-20898-0_31 ]

    http://hal.inria.fr/hal-00950770
  • 14E. Smirnova, K. Balog.

    A user-oriented model for expert finding, in: Proceedings of the 33rd European conference on Advances in information retrieval, Berlin, Heidelberg, ECIR, Springer-Verlag, 2011, pp. 580–592, Best Research Paper.
  • 15D. Tanasa, B. Trousee.

    Advanced Preprocessing for intersites Web Usage Mining, in: IEEE Intelligent Systems, March 2004, vol. 19, no 2, pp. 59-65. [ DOI : 10.1109/MIS.2004.1274912 ]

    http://hal.inria.fr/hal-00950763
  • 16Francisco. de Carvalho, Y. Lechevallier, Filipe M. de Melo.

    Partitioning hard clustering algorithms based on multiple dissimilarity matrices, in: Pattern Recognition, 2012, vol. 45, no 1, pp. 447-464, article-based publishing, article avalaible in 2011.
  • 17Francisco. de Carvalho, Y. Lechevallier.

    Partitional clustering algorithms for symbolic interval data based on single adaptive distances, in: Pattern Recognition, 2009, vol. 42, no 7, pp. 1223-1236.
Publications of the year

Articles in International Peer-Reviewed Journals

  • 18Y. Lechevallier, Francisco. de Carvalho, F. de Melo.

    Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices, in: Fuzzy Sets and Systems, April 2013, vol. 215, pp. 1-28.

    http://hal.inria.fr/hal-00917494
  • 19S. Queiroz, Francisco. de Carvalho, Y. Lechevallier.

    Nonlinear multicriteria clustering based on multiple dissimilarity matrices, in: Pattern Recognition, December 2013, vol. 46, no 12, pp. 3383-3394.

    http://hal.inria.fr/hal-00917496

Invited Conferences

  • 20Francisco. de Carvalho, Y. Lechevallier.

    Partitioning Methods On Dissimilarity Matrices Set, in: European Conference on Data Analysis, Luxembourg, GfKl and SFC, July 2013.

    http://hal.inria.fr/hal-00916906

International Conferences with Proceedings

  • 21M. Bessafi, Francisco. de Carvalho, P. Charton, M. Delsaut, P. Jeanty, T. Despeyroux, J. D. Lan-Sun-Luk, Y. Lechevallier, H. Ralambondrainy, L. Trovalet.

    Clustering of Solar Irradiance, in: European Conference on Data Analysis, Luxembourg, Studies in Classification, Data Analysis, and Knowledge Organization, Springer, July 2013.

    http://hal.inria.fr/hal-00944874
  • 22C. Detraux, D. L. Scapin.

    Utilisabilité d'un Espace Personnel d'Information Modifiable par les Utilisateurs, in: 25ème conférence francophone sur l'Interaction Homme-Machine, IHM'13, Bordeaux, France, ACM, September 2013. [ DOI : 10.1145/2534903.2534916 ]

    http://hal.inria.fr/hal-00877290
  • 23C. Detraux, D. Scapin.

    Personal information systems usability and contents tailoring by users, in: EEE13 - The 2013 International Conference on e-Learning, e-Business, Enterprise Information Systems, and e-Government, Las Vegas, United States, July 2013, pp. 155-161.

    http://hal.inria.fr/hal-00861197
  • 24R. Guan, Y. Lechevallier, H. Wang.

    Adaptive Dynamic Clustering Algorithm for Interval-valued Data based on Squared-Wasserstein Distance, in: Extraction et Gestion des Connaissances, Toulouse, France, C. Vrain, A. Péninou, F. Sedes (editors), Hermann, 2013, vol. RNTI-E-25, pp. 15-30.

    http://hal.inria.fr/hal-00917515
  • 25D. Leprovost, T. Despeyroux, Y. Lechevallier.

    Compréhension de recettes de cuisine utilisateurs par extraction de connaissances intrinsèques, in: EGC'2014 - 14ème conférence Extraction et Gestion des Connaissances, Rennes, France, RNTI, January 2014, vol. E-26.

    http://hal.inria.fr/hal-00945507
  • 26C. Zhang, M. Mazuran, H. Mousavi, Y. Hao, C. Zaniolo, F. Masseglia.

    Mining Frequent Itemsets over Tuple-evolving Data Streams, in: the 28th Annual ACM Symposium on Applied Computing (SAC '13), Coimbra,, Portugal, ACM, New York, NY, USA, March 2013, pp. 267–274. [ DOI : 10.1145/2480362.2480419 ]

    http://hal.inria.fr/hal-00950631
  • 27Francisco. de Carvalho, F. de Melo, Y. Lechevallier.

    A fuzzy c-medoids algorithm based on multiple dissimilarity matrices, in: BRACIS-2013 - 2nd Brazilian Conference on Intelligent Systems, Fortaleza, Brazil, SBC, June 2013, pp. 1-6.

    http://hal.inria.fr/hal-00917502

National Conferences with Proceedings

  • 28M. Bessafi, Francisco. de Carvalho, P. Chartron, M. Delsaut, T. Despeyroux, P. Jeanty, J.-D. Lan Sun Luk, Y. Lechevallier, H. Ralambondrainy, L. Trovalet.

    Classification des journées en fonction des radiations solaires sur l'île de la Réunion, in: 45e journées de la Statistique, Toulouse, France, SfDS, May 2013.

    http://hal.inria.fr/hal-00916915
  • 29D. Leprovost, T. Despeyroux, Y. Lechevallier.

    Langage communautaire, confiance et recettes de cuisine, in: 11ème Atelier sur la Fouille de Données Complexes, Rennes, France, January 2014.

    http://hal.inria.fr/hal-00945514

Scientific Books (or Scientific Book chapters)

  • 30A. Balzanella, Y. Lechevallier, R. Verde.

    Clustering Data Streams by On-Line Proximity Updating, in: Classification and Data Mining, A. Giusti, G. Ritter, M. Vichi (editors), Studies in Classification, Data Analysis, and Knowledge Organization, Springer, December 2013, pp. 97-104. [ DOI : 10.1007/978-3-642-28894-4_12 ]

    http://hal.inria.fr/hal-00917506
  • 31M. Csernel, Francisco. de Carvalho.

    Normalizing Constrained Symbolic Data for Clustering, in: Advances in Theory and Applications of High Dimensional and Symbolic Data Analysis, R. Guan, Y. Lechevallier, G. Saporta, H. Wang (editors), Revue des Nouvelles Technologies de l'Information, Hermann, 2013, vol. RNTI-E-25, pp. 58-77.

    http://hal.inria.fr/hal-00838658
  • 32T. Despeyroux, Francisco. de Carvalho, Y. Lechevallier, F. de Melo.

    Multi-View Clustering on Relational Data, in: Advances in Knowledge Discovery and management, vol. 4, F. Guillet, B. Pinaud, G. Venturini, D. A. Zighed (editors), Studies in Computational Intelligence, Springer International Publishing, June 2013, vol. 527, pp. 37-51. [ DOI : 10.1007/978-3-319-02999-3 ]

    http://hal.inria.fr/hal-00904524
  • 33R. Guan, Y. Lechevallier, H. Wang.

    Adaptive Dynamic Clustering Algorithm for Interval-valued Data based on Squared-Wasserstein Distance, in: Advances in Theory and Applications of High Dimensional and Symbolic Data Analysis, R. Guan, Y. Lechevallier, G. Saporta, H. Wang (editors), Revue des Nouvelles Technologies de l'Information, Hermann, December 2013, vol. RNTI E.25.

    http://hal.inria.fr/hal-00917515
  • 34A. Louati, M.-A. Aufaure, Y. Lechevallier.

    Graph Aggregation: Application to Social Networks, in: Advances in Theory and Applications of High Dimensional and Symbolic Data Analysis, R. Guan, Y. Lechevallier, G. Saporta, H. Wang (editors), Revue des Nouvelles Technologies de l'Information, Hermann, 2013, vol. RNTI-E-25, pp. 157-177.

    http://hal.inria.fr/hal-00838649
  • 35R. Soussi, E. Cuvelier, M.-A. Aufaure, A. Louati, Y. Lechevallier.

    DB2SNA : an All-in-one Tool for Extraction and Aggregation of underlying Social Networks from Relational Databases, in: The influence of technology on social network analysis and Mining, Springer, 2013, pp. 521-546.

    http://hal.inria.fr/hal-00830599

Books or Proceedings Editing

  • 36Y. Lechevallier, G. Saporta, H. Wang, R. Guan.

    R. Guan, Y. Lechevallier, G. Saporta, H. Wang (editors), Advances in Theory and Applications of High Dimensional and Symbolic Data Analysis, Revue des Nouvelles Technologies de l'Information, Hermann, December 2013, vol. RNTI E.25, 197 p.

    http://hal.inria.fr/hal-00917511

Internal Reports

  • 37M. Conte, G. Monteleone, M. Megliola, B. Trousse, C. Tiffon, D. Colombo Verga, S. Vicini, M. Kalverkamp, M. Pallot, A. Vilmos, K. Furdik, R. Nikolov.

    ELLIOT Project Presentation #4, Apr 2013, 44 p, Delivrable D6.3.4 (M32).

    http://hal.inria.fr/hal-00943988
  • 38M. Tiemann, A. Badii, M. Kalverkamp, S. Vinci, B. Trousse, C. Tiffon, X. Augros, G. Pilot, F. Bonacina, Y. Lechevallier, A.-L. Negri.

    Report on IOT Living Labs Continuous Exploration and Evaluation (final), 2013, no Livrable D4.3.1, Elliot Delivrable D4.3.3.

    http://hal.inria.fr/hal-00940078
References in notes
  • 39H.-H. Bock, E. Diday (editors)

    Analysis of Symbolic Data. Exploratory methods for extracting statistical information from complex data, Springer Verlag, 2000.
  • 40H. Behja.

    Plateforme objet d'évaluation orientée point de vue d'un système d'information, University of Casablanca, Morocco, 2009.
  • 41M. Chavent, Y. Lechevallier, O. Briant.

    DIVCLUS-T: A monothetic divisive hierarchical clustering method, in: Computational Statistics and Data Analysis, 2007, vol. 52, pp. 687-701.
  • 42S. Chelcea.

    Agglomerative 2-3 Hierarchical Classification: Theoretical and Applicative Study, University of Nice Sophia Antipolis, March 2007, PhD Thesis.

    http://tel.archives-ouvertes.fr/tel-00156809/fr/
  • 43B. Conan-Guez, F. Rossi.

    Speeding Up the Dissimilarity Self-Organizing Maps by Branch and Bound, in: Proceedings of 9th International Work-Conference on Artificial Neural Networks (IWANN 2007), San Sebastian (Spain), F. Sandoval, A. Prieto, J. Cabestany, M. Grana (editors), Lecture Notes in Computer Science, June 2007, no 4507, pp. 203–210.
  • 44B. Conan-Guez, F. Rossi, A. El Golli.

    Fast Algorithm and Implementation of Dissimilarity Self-Organizing Maps, in: Neural Networks, August 2006, vol. 19, no 6–7, pp. 855–863.

    http://hal.inria.fr/inria-00174196
  • 45A. Da Silva, Y. Lechevallier, F. Rossi, Francisco. de Carvalho.

    Clustering Dynamic Web Usage Data, in: CoRR, 2012, vol. abs/1201.0963.
  • 46A. Da Silva, Y. Lechevallier, R. Seraoui.

    A Clustering Approach to Monitor System Working, in: Learning and Data Science, M. Gettler Summa, L. Bottou, B. Goldfarb, F. Murtagh, C. Pardoux, M. Touati (editors), CRC Computer Science & Data Analysis, Chapman & Hall, 2011, vol. K13059, chap. 10.
  • 47T. Despeyroux, Y. Lechevallier, Francisco. de Carvalho, F. de Melo.

    Un algorithme de classification automatique pour des données relationnelles multi-vues, in: EGC 2012 - Extraction et Gestion des Connaissances 2012, Bordeaux, France, Y. Lechevallier, G. Melançon, B. Pinaud (editors), Revue des Nouvelles Technologies de l'Information, Hermann, January 2012, vol. E.23, pp. 125-136.

    http://hal.inria.fr/hal-00697118
  • 48C. Detraux, D. L. Scapin.

    Evaluation Ergonomique Initiale du prototype V0.1 version Mobile, 2012, no Livrable D3.2.1b.

    http://hal.inria.fr/hal-00796106
  • 49C. Detraux, D. L. Scapin.

    Evaluation Ergonomique Initiale du prototype V0.1 version PC, 2012, no Livrable D3.2.1a, ANR-PIMI.

    http://hal.inria.fr/hal-00796115
  • 50M. E. Fayad, D. C. Schmidt.

    Object-Oriented Application Frameworks, in: Communication of the ACM, 1997, vol. 40, no 10, pp. 32-38.
  • 51C. Goffart, B. Senach, B. Trousse.

    Protocole d'expérimentation - version finale, november 2011, Pacalabs Ecoffices Déliverable 1.3.
  • 52A. Gomes Da Silva.

    Analyse des données évolutives : application aux données d'usage du Web, Université Paris Dauphine - Paris IX, September 2009, Prix Simon Régnier 2010- SFC association.

    http://tel.archives-ouvertes.fr/tel-00445501
  • 53M. Jaczynski.

    Modèle et plate-forme à objets pour l'indexation des cas par situation comportementale: application à l'assistance à la navigation sur le Web, Université de Nice Sophia-Antipolis, Sophia-Antipolis, December 1998.
  • 54M. Jaczynski, B. Trousse.

    Patrons de conception dans la modélisation d'une plate-forme po ur le raisonnement à partir de cas, in: Revue l'Objet, 1999, vol. 5, no 2, Numéro Spécial sur les patterns orientés objets, D. Rieu et J-P. Giraudon (guest editors).
  • 55R. E. Johnson, B. Foote.

    Designing Reusable Classes, in: Journal of Object-oriented programming, 1988, vol. 1, no 2, pp. 22–35.
  • 56D. Krathwohl, B. Bloom, B. Masia.

    Taxonomy of Educational Objectives, the classification of Educational Goals, 1973, Handbook 2: Affective domain. New York: David McKay Co., Inc.
  • 57A. Marascu.

    Extraction de motifs séquentiels dans les flux de données, University of Nice Sophia Antipolis, 2009, PhD Thesis.

    http://tel.archives-ouvertes.fr/tel-00445894/fr/
  • 58A. Marascu, F. Masseglia, Y. Lechevallier.

    REGLO : une nouvelle stratégie pour résumer un flux de séries temporelles, in: EGC, 2010, pp. 217-228.
  • 59A. Marascu, F. Masseglia.

    Atypicity detection in data streams: A self-adjusting approach, in: Intelligent Data Analysis, 2011, vol. 15, no 1, pp. 89-105.
  • 60M. Noirhomme-Fraiture, E. Diday.

    Symbolic Data Analysis and the SODAS Software, Wiley Interscience, Chichester, 2008, 457 p.
  • 61M. Noirhomme-Fraiture.

    User manual for SODAS 2 Software, FUNDP, Belgique, april 2004, version 1.0.
  • 62H. Oinas-Kukkonen, M. Harjumaa.

    Persuasive Systems Design: Key Issues, Process Model, and System Features, in: Communications of the Association for Information Systems, 2009, vol. 24.
  • 63M. Pallot, K. Pawar.

    Holistic Model of User Experience for Living Lab Experiential Design, in: "Proceedings of the 18th International Conference on Engineering, Technology and Innovation, ICE2012 Innovation by Collaboration and Entrepreneurial Partnerships", Munich, Germany, june 2012.
  • 64F. Rossi, B. Conan-Guez.

    Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis, in: Neural Networks, January 2005, vol. 18, no 1, pp. 45–60.

    http://hal.inria.fr/inria-00000599
  • 65F. Rossi, B. Conan-Guez.

    Un modèle neuronal pour la régression et la discrimination sur données fonctionnelles, in: Revue de Statistique Appliquée, 2005, vol. LIII, no 4, pp. 5–30.

    http://hal.inria.fr/inria-00001190
  • 66F. Rossi, B. Conan-Guez.

    Theoretical Properties of Projection Based Multilayer Perceptrons with Functional Inputs, in: Neural Processing Letters, February 2006, vol. 23, no 1, pp. 55–70.

    http://hal.inria.fr/inria-00001191
  • 67F. Rossi, N. Delannay, B. Conan-Guez, M. Verleysen.

    Representation of Functional Data in Neural Networks, in: Neurocomputing, March 2005, vol. 64, pp. 183–210.

    http://hal.inria.fr/inria-00000666
  • 68F. Rossi, A. Hasenfuss, B. Hammer.

    Accelerating Relational Clustering Algorithms With Sparse Prototype Representation, in: Proceedings of the 6th International Workshop on Self-Organizing Maps (WSOM 07), Bielefeld (Germany), September 2007, ISBN: 978-3-00-022473-7.

    http://dx.doi.org/10.2390/biecoll-wsom2007-144
  • 69F. Rossi.

    Model collisions in the dissimilarity SOM, in: Proceedings of XVth European Symposium on Artificial Neural Networks (ESANN 2007), Bruges (Belgium), April 2007, pp. 25–30.

    http://apiacoa.org/publications/2007/dsom-collision-esann.pdf
  • 70V. Roto, E. Law, A. Vermeeren, J. Hoonhout.

    User Experience White Paper: Bringing clarity to the concept of user experience, 2011, (Result from Dagstuhl Seminar on Demarcating User Experience, Sept. 15-18, 2010).

    http://www.allaboutux.org/uxwhitepaper
  • 71D. L. Scapin.

    Préface, in: Ergonomie des Logiciels. Recueil de Normes Ergonomie des postes et lieux de travail, AFNOR, 2012.
  • 72D. Tanasa.

    Web Usage Mining: Contributions to Intersites Logs Preprocessing and Sequential Pattern Extraction with Low Support, University of Nice Sophia Antipolis, june 2005, PhD Thesis.

    http://tel.archives-ouvertes.fr/tel-00178870/fr/
  • 73N. Villa, F. Rossi.

    A comparison between dissimilarity SOM and kernel SOM for clustering the vertices of a graph, in: Proceedings of the 6th International Workshop on Self-Organizing Maps (WSOM 07), Bielefeld (Germany), September 2007, ISBN: 978-3-00-022473-7.

    http://dx.doi.org/10.2390/biecoll-wsom2007-139
  • 74E. M. Zemmouri, H. Behja, B. Ouhbi, B. Trousse, A. Marzak, Y. Benghabrit.

    Goal-Driven Approach to Model Interaction between Viewpoints of a Multi-View KDD process, in: 4th Conference on next generation networks & Services, 2012.
  • 75E. M. Zemmouri.

    Représentation et gestion des connaissances dans un processus d'Extraction de Connaissances à partir de Données multi-points de vue, Ecole Nationale Supérieure d'Arts et Métiers - Meknès, December 2013.

    http://tel.archives-ouvertes.fr/tel-00940780
  • 76G. Zichermann, C. Cunningham.

    Gamification by design, O'Reilly, 2011.
  • 77Francisco. de Carvalho, R.M.C.R. De Souza, M. Chavent, Y. Lechevallier.

    Adaptive Hausdorff distances and dynamic clustering of symbolic interval data, in: Pattern Recognition Letters, February 2006, vol. 27, no 3, pp. 167–179.
  • 78Francisco. de Carvalho, Y. Lechevallier, Filipe M. de Melo.

    Partitioning hard clustering algorithms based on multiple dissimilarity matrices, in: Pattern Recognition, 2012, vol. 45, no 1, pp. 447-464.
  • 79Francisco. de Carvalho, Y. Lechevallier, R. Verde.

    Clustering methods in symbolic data analysis, in: Symbolic Data Analysis and the SODAS Software, E. Diday, M. Noirhomme-Fraiture (editors), Wiley, 2008, pp. 181-204.
  • 80Francisco. de Carvalho, Y. Lechevallier, Filipe M. de Melo.

    Partitioning hard clustering algorithms based on multiple dissimilarity matrices, in: Pattern Recognition, 2011, vol. 45, no 1, pp. 447-464.