Gordon, AD.Classification.London: Chapman and Hall; 1981.
March, ST.Techniques for structuring database records.ACM Comput Surv 1983, 15:45–79.
Jain, AK,Dubes, RC.Algorithms For Clustering Data. Englwood Cliffs, NJ: Prentice‐Hall; 1988.
Gordon, AD.A review of hierarchical classification.J R Stat Soc A 1987, 150:119–137
Mirkin, B.Mathematical Classification and Clustering.Dordrecht, The Netherlands: Kluwer; 1996.
Jain, AK,Murty, MN, Flynn PJ. Data clustering: a review.ACM Comput Surv 1999, 31:264–323
Xu, R,Wunsch, D.Survey of clustering algorithms.IEEE Trans Neural Netw 2005, 16:645–678
Lerman, IC.Classification et Analyse Ordinale des Données.Paris: Dunod; 1981.
Janowitz, MF.Ordinal and Relational Clustering.Singapore: World Scientific; 2010.
vanRijsbergen, CJ.Information Retrieval. 2nd ed.London: Butterworths; 1979.
Griffiths, A,Robinson, LA,Willett, P.Hierarchic agglomerative clustering methods for automatic document classification.J Doc 1984, 40:175–205
Murtagh, F.Symmetry in data mining and analysis: a unifying view based on hierarchy.Proc Steklov Inst Math 2009, 265:177–198
Anderberg, MR.Cluster Analysis for Applications.New York: Academic Press; 1973.
Deza, MM,Deza, E.Encyclopedia of Distances.Berlin: Springer; 2009.
Benzécri, JP.L`Analyse des Données. I. La Taxinomie. 3rd ed.Paris: Dunod; 1979.
LeRoux, B,Rouanet, H.Geometric Data Analysis: From Correspondence Analysis to Structured Data Analysis.Dordrecht: Kluwer; 2004.
Murtagh, F.Correspondence Analysis and Data Coding with Java and R.Boca Raton, FL: Chapman and Hall; 2005.
Graham, RH,Hell, P.On the history of the minimum spanning tree problem.Ann Hist Comput 1985, 7:43–57
Blashfield, RK,Aldenderfer, MS.The literature on cluster analysisMultivariate Behav Res 1978, 13:271–295.
Sneath, PHA,Sokal, RR.Numerical Taxonomy.San Francisco, CA: Freeman; 1973.
Rapoport, A,Fillenbaum, S.An experimental study of semantic structures. In:Romney, AK,Shepard, RN,Nerlove, SB, eds.Multidimensional Scaling: Theory and Applications in the Behavioral Sciences. Vol. 2, Applications.New York: Seminar Press; 1972, 93–131.
Murtagh, F.The Haar wavelet transform of a dendrogram.J Classif 2007, 24:3–32
Wishart, D.Mode analysis: a generalization of nearest neighbour which reduces chaining effects. In:Cole, AJ, ed.Numerical Taxonomy.New York: Academic Press; 1969, 282–311.
Sibson, R.SLINK: an optimally efficient algorithm for the single link cluster method.Comput J 1973, 16:30–34
Rohlf, FJ.Algorithm 76: hierarchical clustering using the minimum spanning tree.Comput J 1973, 16:93–95
Defays, D.An efficient algorithm for a complete link method.Comput J 1977, 20:364–366
deRham, C.La classification hiérarchique ascendante selon la méthode des voisins réciproques.Les Cahiers de l`Analyse des Données1980, V:135–144.
Juan, J.Programme de classification hiérarchique par l`algorithme de la recherche en chaîne des voisins réciproques.Les Cahiers de l`Analyse des Données1982, VII:219–225.
Murtagh, F.A survey of recent advances in hierarchical clustering algorithms.Comput J 1983, 26:354–359
Murtagh, F.Multidimensional Clustering Algorithms.Würzburg, Germany: Physica‐Verlag; 1985.
Bruynooghe, M.Méthodes nouvelles en classification automatique des données taxinomiques nombreuses.Statistique et Analyse des Données 1977, 3:24–42
Murtagh, F.Complexities of hierarchic clustering algorithms: state of the art.Comput Stat Quart 1984, 1:101–113
Day, WHE,Edelsbrunner, H.Efficient algorithms for agglomerative hierarchical clustering methods.J Classif 1984, 1:7–24
Willett, P.Efficiency of hierarchic agglomerative clustering using the ICL distributed array processor.J Doc 1989, 45:1–45
Gillet, VJ,Wild, DJ,Willett, P,Bradshaw, J.Similarity and dissimilarity methods for processing chemical structure databases.Comput J 1998, 41:547–558
White, HD,McCain, KW.Visualization of literatures.Annu Rev Inform Sci Technol 1997, 32:99–168
Kohonen, T.Self‐Organization and Associative Memory.Berlin: Springer; 1984.
Kohonen, T.Self‐Organizing Maps. 3rd ed.Berlin: Springer; 2001.
Murtagh, F,Hernández‐Pajares, M.The Kohonen self‐organizing map method: an assessment.J Classif 1995, 12:165–190
Lampinen, J,Oja, E.Clustering properties of hierarchical self‐organizing maps.J Math Imaging Vision 1992, 2:261–272
Dittenbach, M,Rauber, A,Merkl, D.Uncovering the hierarchical structure in data using the growing hierarchical self‐organizing map.Neurocomputing,2002, 48:199–216.
Endo, M,Ueno, M,Tanabe, T.A clustering method using hierarchical self‐organizing maps.J VLSI Signal Process 2002, 32:105–118
Miikkulainien, R.Script recognition with hierarchical feature maps.Connect Sci 1990, 2:83–101
Vicente, D,Vellido, A.Review of hierarchical models for data clustering and visualization. In:Giráldez, R,Riquelme, JC,Aguilar‐Ruiz, JS, eds.Tendencias de la Minería de Datos en España.Seville, Spain: Red Española de Minería de Datos; 2004.
Tino, P,Nabney, I.Hierarchical GTM: constructing localized non‐linear projection manifolds in a principled way.IEEE Trans Pattern Anal Mach Intell 2002, 24:639–656
Wang, Y,Freedman, MI,Kung S‐Y. Probabilistic principal component subspaces: a hierarchical finite mixture model for data visualization.IEEE Trans Neural Netw 2000, 11:625–636
Murtagh, F,Raftery, AE,Starck, JL.Bayesian inference for multiband image segmentation via model‐based clustering trees.Image Vision Comput 2005, 23:587–596
vonLuxburg, U.A tutorial on spectral clustering.Stat Comput 1997, 17:395–416
Gondran, M.Valeurs propres et vecteurs propres en classification hiérarchique.RAIRO Informatique Théorique 1976, 10:39–46
Xu, R,Wunsch, DC.Clustering.IEEE Computer Society Press; 2008.
Grabusts, P,Borisov, A.Using grid‐clustering methods in data classification. In:PARELEC `02: Proceedings of the International Conference on Parallel Computing in Electrical Engineering.Washington, D.C.: IEEE Computer Society; 2002.
Wang, W,Yang, J,Muntz, R.STING: a statistical information grid approach to spatial data mining. In:VLDB `97: Proceedings of the 23rd International Conference on Very Large Data Bases.San Francisco, CA: Morgan Kaufmann Publishers Inc.; 1997, 18–195.
Hinneburg, A,Keim, D.Optimal grid‐clustering: towards breaking the curse of dimensionality in high‐dimensional clustering. In:VLDB `99: Proceedings of the 25th International Conference on Very Large Data Bases.San Francisco, CA: Morgan Kaufmann Publishers Inc.; 1999, 506–517.
Gan, G,Ma, C,Wu, J.Data Clustering Theory, Algorithms, and Applications.Philadelphia, PA: SIAM;2007.
Schikuta, E.Grid‐clustering: an efficient hierarchical clustering method for very large data sets. In:ICPR `96: Proceedings of the 13th International Conference on Pattern Recognition.Washington, D.C.: IEEE Computer Society; 1996, 101–105.
Sheikholeslami, G,Chatterjee, S,Zhang, A.Wavecluster: a wavelet based clustering approach for spatial data in very large databases.VLDB J 2000, 8:289–304
Chang, J‐W,Jin, D‐S.A new cell‐based clustering method for large, high‐dimensional data in data mining applications. In:SAC `02: Proceedings of the 2002 ACM Symposium on Applied Computing.New York: ACM, 2002, 503–507.
Park, NH,Lee, WS.Statistical grid‐based clustering over data streams.SIGMOD Rec 2004, 33:32–37
Ester, M,Kriegel H‐P,Sander, J,Xu, X.A density‐based algorithm for discovering clusters in large spatial databases with noise, In:Second International Conference on Knowledge Discovery and Data Mining.Menlo Park, CA: AAAI Press; 1996, 226–231.
Sander, J,Ester, M,Kriegel, H‐P,Xu, X.Density‐based clustering in spatial databases: the algorithm GDBSCAN and its applications.Data Min Knowl Discov 1998, 2:169–194
Xu, X,Jäger, J,Kriegel, H‐P.A fast parallel clustering algorithm for large spatial databases.Data Min Knowl Discov 1999, 3:263–290
Zaïane, OR,Lee, C‐H.Clustering spatial data in the presence of obstacles: a density‐based approach. In:IDEAS `02: Proceedings of the 2002 International Symposium on Database Engineering and Applications.Washington, D.C.: IEEE Computer Society; 2002, 214–223.
Dash, M,Liu, H,Xu, X.1 + 1 %3E 2: Merging distance and density based clustering. In:DASFAA `01: Proceedings of the 7th International Conference on Database Systems for Advanced Applications.Washington, D.C.: IEEE Computer Society;2001, 32–39.
Xu, X,Ester, M,Kriegel, H‐P,Sander, J.A distribution‐based clustering algorithm for mining in large spatial databases. In:ICDE `98: Proceedings of the Fourteenth International Conference on Data Engineering.Washington, D.C.: IEEE Computer Society,1998, 324–331.
Hinneburg, A,Keim, DA.A density‐based algorithm for discovering clusters in large spatial databases with noise. In:Proceeding of the 4th International Conference on Knowledge Discovery and Data Mining.New York: AAAI Press; 1998, 58–68.
Wang, L,Wang, Z‐O.CUBN: a clustering algorithm based on density and distance. In:Proceeding of the 2003 International Conference on Machine Learning and Cybernetics.IEEE Press; 2003, 108–112.
Murtagh, F,Downs, G,Contreras, P.Hierarchical clustering of massive, high dimensional data sets by exploiting ultrametric embedding.SIAM J Sci Comput 2008, 30:707–730
Contreras, P,Murtagh, F.Fast hierarchical clustering from the Baire distance. In:Hocarek‐Junge, H,Weihs, C, eds.Classification as a Tool for Research.Berlin: Springer; 2010, 235–243.
Contreras, P.Search and Retrieval in Massive Data Collections. PhD Thesis.Egham, UK: Royal Holloway, University of London; 2010.
