Memberships Networks for High-Dimensional Fuzzy Clustering Visualization

Visualizing the cluster structure of high-dimensional data is a non-trivial task that must be able to deal with the large dimensionality of the input data. Unlike hard clustering structures, visualization of fuzzy clusterings is not as straightforward because soft clustering algorithms yield more co...

Full description

Autores:

Tipo de recurso:

Fecha de publicación:: 2019

Institución:: Universidad de Medellín

Repositorio:: Repositorio UDEM

Idioma:: eng

id	REPOUDEM2_3527b66592cda38773a645b7fa9f2fe7
oai_identifier_str	oai:repository.udem.edu.co:11407/5662
network_acronym_str	REPOUDEM2
network_name_str	Repositorio UDEM
repository_id_str
dc.title.none.fl_str_mv	Memberships Networks for High-Dimensional Fuzzy Clustering Visualization
title	Memberships Networks for High-Dimensional Fuzzy Clustering Visualization
spellingShingle	Memberships Networks for High-Dimensional Fuzzy Clustering Visualization Clustering visualization Fuzzy clustering High-dimensional data Membership network Cluster analysis Complex networks Data visualization Fuzzy clustering Input output programs Large dataset Visualization Cluster structure Financial profiles Hard clustering High dimensional data High-dimensional Non-trivial tasks Simple networks Weighted networks Clustering algorithms
title_short	Memberships Networks for High-Dimensional Fuzzy Clustering Visualization
title_full	Memberships Networks for High-Dimensional Fuzzy Clustering Visualization
title_fullStr	Memberships Networks for High-Dimensional Fuzzy Clustering Visualization
title_full_unstemmed	Memberships Networks for High-Dimensional Fuzzy Clustering Visualization
title_sort	Memberships Networks for High-Dimensional Fuzzy Clustering Visualization
dc.subject.none.fl_str_mv	Clustering visualization Fuzzy clustering High-dimensional data Membership network Cluster analysis Complex networks Data visualization Fuzzy clustering Input output programs Large dataset Visualization Cluster structure Financial profiles Hard clustering High dimensional data High-dimensional Non-trivial tasks Simple networks Weighted networks Clustering algorithms
topic	Clustering visualization Fuzzy clustering High-dimensional data Membership network Cluster analysis Complex networks Data visualization Fuzzy clustering Input output programs Large dataset Visualization Cluster structure Financial profiles Hard clustering High dimensional data High-dimensional Non-trivial tasks Simple networks Weighted networks Clustering algorithms
description	Visualizing the cluster structure of high-dimensional data is a non-trivial task that must be able to deal with the large dimensionality of the input data. Unlike hard clustering structures, visualization of fuzzy clusterings is not as straightforward because soft clustering algorithms yield more complex clustering structures. Here is introduced the concept of membership networks, an undirected weighted network constructed based on the fuzzy partition matrix that represents a fuzzy clustering. This simple network-based method allows understanding visually how elements involved in this kind of complex data clustering structures interact with each other, without relying on a visualization of the input data themselves. Experiment results demonstrated the usefulness of the proposed method for the exploration and analysis of clustering structures on the Iris flower data set and two large and unlabeled financial datasets, which describes the financial profile of customers of a local bank. © 2019, Springer Nature Switzerland AG.
publishDate	2019
dc.date.accessioned.none.fl_str_mv	2020-04-29T14:53:35Z
dc.date.available.none.fl_str_mv	2020-04-29T14:53:35Z
dc.date.none.fl_str_mv	2019
dc.type.eng.fl_str_mv	Conference Paper
dc.type.coarversion.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/article
dc.identifier.isbn.none.fl_str_mv	9783030310189
dc.identifier.issn.none.fl_str_mv	18650929
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/11407/5662
dc.identifier.doi.none.fl_str_mv	10.1007/978-3-030-31019-6_23
identifier_str_mv	9783030310189 18650929 10.1007/978-3-030-31019-6_23
url	http://hdl.handle.net/11407/5662
dc.language.iso.none.fl_str_mv	eng
language	eng
dc.relation.isversionof.none.fl_str_mv	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075665935&doi=10.1007%2f978-3-030-31019-6_23&partnerID=40&md5=cc5318918e57cf763413520330bcc88a
dc.relation.citationvolume.none.fl_str_mv	1052
dc.relation.citationstartpage.none.fl_str_mv	263
dc.relation.citationendpage.none.fl_str_mv	273
dc.relation.references.none.fl_str_mv	Abonyi, J., Babuska, R., FUZZSAM-visualization of fuzzy clustering results by modified Sammon mapping (2004) IEEE International Conference on Fuzzy Systems, 1, pp. 365-370. , https://doi.org/10.1109/FUZZY.2004.1375750, vol., pp Bécavin, C., Benecke, A., New dimensionality reduction methods for the representation of high dimensional omics data (2011) Expert Rev. Mol. Diagn., 11 (1), pp. 27-34. , https://doi.org/10.1586/erm.10.95 Berthold, M.R., Wiswedel, B., Patterson, D.E., Interactive exploration of fuzzy clusters using neighborgrams (2005) Fuzzy Sets Syst, 149 (1), pp. 21-37. , https://doi.org/10.1016/j.fss.2004.07.009 Everitt, B.S., Landau, S., Leese, M., Stahl, D., (2011) Cluster Analysis, , Wiley, Hoboken Feil, B., Balasko, B., Abonyi, J., Visualization of fuzzy clusters by fuzzy Sammon mapping projection: Application to the analysis of phase space trajectories (2007) Soft Comput, 11 (5), pp. 479-488. , https://doi.org/10.1007/s00500-006-0111-5 Fortunato, S., Hric, D., Community detection in networks: A user guide (2016) Phys. Rep., 659, pp. 1-44. , https://doi.org/10.1016/j.physrep.2016.09.002 Francalanci, C., Hussain, A., Influence-based Twitter browsing with NavigTweet (2017) Inf. Syst., 64, pp. 119-131. , https://doi.org/10.1016/j.is.2016.07.012 Fruchterman, T.M., Reingold, E.M., Graph drawing by force-directed placement (1991) Softw. Pract. Exp., 21 (11), pp. 1129-1164. , https://doi.org/10.1002/spe.4380211102 Gajer, P., Goodrich, M.T., Kobourov, S.G., A multi-dimensional approach to force-directed layouts of large graphs (2004) Comput. Geom., 29 (1), pp. 3-18. , https://doi.org/10.1016/j.comgeo.2004.03.014 Gibson, H., Faith, J., Vickers, P., A survey of two-dimensional graph layout techniques for information visualisation (2013) Inf. Vis., 12 (3-4), pp. 324-357. , https://doi.org/10.1177/1473871612455749 Heberle, H., Carazzolle, M.F., Telles, G.P., Meirelles, G.V., Minghim, R., Cell NetVis: A web tool for visualization of biological networks using force-directed layout constrained by cellular components (2017) BMC Bioinform, 18. , https://doi.org/10.1186/s12859-017-1787-5 Höppner, F., Klawonn, F., Visualising clusters in high-dimensional data sets by intersecting spheres Proceedings of 2006 International Symposium on Evolving Fuzzy Systems, EFS 2006 Hu, Y., Shi, L., Visualizing large graphs (2015) Wiley Interdiscip. Rev. Comput. Stat., 7 (2), pp. 115-136. , https://doi.org/10.1002/wics.1343 Ishida, Y., Itoh, T., A force-directed visualization of conversation logs (2017) Proceedings of Computer Graphics International Conference-Cgi 2017, pp. 1-5. , https://doi.org/10.1145/3095140.3095156, pp., ACM Press, New York Jacomy, M., Venturini, T., Heymann, S., Bastian, M., ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software (2014) Plos ONE, 9 (6), pp. 1-12. , https://doi.org/10.1371/journal.pone.0098679 Leisch, F., A toolbox for K-centroids cluster analysis (2006) Comput. Stat. Data Anal., 51 (2), pp. 526-544. , https://doi.org/10.1016/j.csda.2005.10.006 Leisch, F., Neighborhood graphs, stripes and shadow plots for cluster visualization (2010) Stat. Comput., 20 (4), pp. 457-469. , https://doi.org/10.1007/s11222-009-9137-8 van der Maaten, L., Accelerating t-SNE using tree-based algorithms (2014) J. Mach. Learn. Res., 15, pp. 3221-3245. , http://jmlr.org/papers/v15/vandermaaten14a.html van der Maaten, L., Hinton, G., Visualizing high-dimensional data using t-SNE (2008) J. Mach. Learn. Res., 9, pp. 2579-2605. , http://www.jmlr.org/papers/v9/vandermaaten08a.html Martin, S., Brown, W.M., Klavans, R., Boyack, K.W., OpenOrd: An open-source toolbox for large graph layout (2011) Proceedings of SPIE, P, 7868. , https://doi.org/10.1117/12.871402, January Metsalu, T., Vilo, J., ClustVis: A web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap (2015) Nucleic Acids Res, 43 (W1), pp. W566-W570. , https://doi.org/10.1093/nar/gkv468 Newman, M.E.J., The structure and function of complex networks (2003) SIAM Rev, 45 (2), pp. 167-256. , https://doi.org/10.1137/S003614450342480 Pison, G., Struyf, A., Rousseeuw, P.J., Displaying a clustering with CLUSPLOT (1999) Comput. Stat. Data Anal., 30 (4), pp. 381-392. , https://doi.org/10.1016/S0167-9473(98)00102-9 Sato-Ilic, M., Ilic, P., Visualization of fuzzy clustering result in metric space (2016) Proc. Comput. Sci., 96, pp. 1666-1675. , https://doi.org/10.1016/j.procs.2016.08.214 Serra, A., Galdi, P., Tagliaferri, R., Machine learning for bioinformatics and neu-roimaging (2018) Wiley Interdisc. Rev.: Data Min. Knowl. Discov, 8 (5), pp. 1-33. , https://doi.org/10.1002/widm.1248 Sharko, J., Grinstein, G., Visualizing fuzzy clusters using RadViz (2009) Proceedings of International Conference Information Visualisation, pp. 307-316. , https://doi.org/10.1109/IV.2009.74, pp Wang, K.J., Yan, X.H., Chen, L.F., Geometric double-entity model for recognizing far-near relations of clusters (2011) Sci. China Inf. Sci., 54 (10), pp. 2040-2050. , https://doi.org/10.1007/s11432-011-4386-5 Wang, W., Zhang, Y., On fuzzy cluster validity indices (2007) Fuzzy Sets Syst, 158 (19), pp. 2095-2117. , https://doi.org/10.1016/j.fss.2007.03.004 Xu, R., Wunsch, D., Survey of clustering algorithms (2005) IEEE Trans. Neural Netw., 16 (3), pp. 645-678. , https://doi.org/10.1109/TNN.2005.845141 Xu, R., Wunsch, D.C., Clustering algorithms in biomedical research: A review (2010) IEEE Rev. Biomed. Eng., 3, pp. 120-154. , https://doi.org/10.1109/RBME.2010.2083647 Zhou, F., A radviz-based visualization for understanding fuzzy clustering results (2017) Proceedings of 10Th International Symposium on Visual Information Communication and Interaction, pp. 9-15. , https://doi.org/10.1145/3105971.3105980, pp., ACM, New York
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_16ec
rights_invalid_str_mv	http://purl.org/coar/access_right/c_16ec
dc.publisher.none.fl_str_mv	Springer
dc.publisher.program.none.fl_str_mv	Ingeniería de Sistemas
dc.publisher.faculty.none.fl_str_mv	Facultad de Ingenierías
publisher.none.fl_str_mv	Springer
dc.source.none.fl_str_mv	Communications in Computer and Information Science
institution	Universidad de Medellín
repository.name.fl_str_mv	Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv	repositorio@udem.edu.co
_version_	1814159121737318400
spelling	20192020-04-29T14:53:35Z2020-04-29T14:53:35Z978303031018918650929http://hdl.handle.net/11407/566210.1007/978-3-030-31019-6_23Visualizing the cluster structure of high-dimensional data is a non-trivial task that must be able to deal with the large dimensionality of the input data. Unlike hard clustering structures, visualization of fuzzy clusterings is not as straightforward because soft clustering algorithms yield more complex clustering structures. Here is introduced the concept of membership networks, an undirected weighted network constructed based on the fuzzy partition matrix that represents a fuzzy clustering. This simple network-based method allows understanding visually how elements involved in this kind of complex data clustering structures interact with each other, without relying on a visualization of the input data themselves. Experiment results demonstrated the usefulness of the proposed method for the exploration and analysis of clustering structures on the Iris flower data set and two large and unlabeled financial datasets, which describes the financial profile of customers of a local bank. © 2019, Springer Nature Switzerland AG.engSpringerIngeniería de SistemasFacultad de Ingenieríashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85075665935&doi=10.1007%2f978-3-030-31019-6_23&partnerID=40&md5=cc5318918e57cf763413520330bcc88a1052263273Abonyi, J., Babuska, R., FUZZSAM-visualization of fuzzy clustering results by modified Sammon mapping (2004) IEEE International Conference on Fuzzy Systems, 1, pp. 365-370. , https://doi.org/10.1109/FUZZY.2004.1375750, vol., ppBécavin, C., Benecke, A., New dimensionality reduction methods for the representation of high dimensional omics data (2011) Expert Rev. Mol. Diagn., 11 (1), pp. 27-34. , https://doi.org/10.1586/erm.10.95Berthold, M.R., Wiswedel, B., Patterson, D.E., Interactive exploration of fuzzy clusters using neighborgrams (2005) Fuzzy Sets Syst, 149 (1), pp. 21-37. , https://doi.org/10.1016/j.fss.2004.07.009Everitt, B.S., Landau, S., Leese, M., Stahl, D., (2011) Cluster Analysis, , Wiley, HobokenFeil, B., Balasko, B., Abonyi, J., Visualization of fuzzy clusters by fuzzy Sammon mapping projection: Application to the analysis of phase space trajectories (2007) Soft Comput, 11 (5), pp. 479-488. , https://doi.org/10.1007/s00500-006-0111-5Fortunato, S., Hric, D., Community detection in networks: A user guide (2016) Phys. Rep., 659, pp. 1-44. , https://doi.org/10.1016/j.physrep.2016.09.002Francalanci, C., Hussain, A., Influence-based Twitter browsing with NavigTweet (2017) Inf. Syst., 64, pp. 119-131. , https://doi.org/10.1016/j.is.2016.07.012Fruchterman, T.M., Reingold, E.M., Graph drawing by force-directed placement (1991) Softw. Pract. Exp., 21 (11), pp. 1129-1164. , https://doi.org/10.1002/spe.4380211102Gajer, P., Goodrich, M.T., Kobourov, S.G., A multi-dimensional approach to force-directed layouts of large graphs (2004) Comput. Geom., 29 (1), pp. 3-18. , https://doi.org/10.1016/j.comgeo.2004.03.014Gibson, H., Faith, J., Vickers, P., A survey of two-dimensional graph layout techniques for information visualisation (2013) Inf. Vis., 12 (3-4), pp. 324-357. , https://doi.org/10.1177/1473871612455749Heberle, H., Carazzolle, M.F., Telles, G.P., Meirelles, G.V., Minghim, R., Cell NetVis: A web tool for visualization of biological networks using force-directed layout constrained by cellular components (2017) BMC Bioinform, 18. , https://doi.org/10.1186/s12859-017-1787-5Höppner, F., Klawonn, F., Visualising clusters in high-dimensional data sets by intersecting spheres Proceedings of 2006 International Symposium on Evolving Fuzzy Systems, EFS 2006Hu, Y., Shi, L., Visualizing large graphs (2015) Wiley Interdiscip. Rev. Comput. Stat., 7 (2), pp. 115-136. , https://doi.org/10.1002/wics.1343Ishida, Y., Itoh, T., A force-directed visualization of conversation logs (2017) Proceedings of Computer Graphics International Conference-Cgi 2017, pp. 1-5. , https://doi.org/10.1145/3095140.3095156, pp., ACM Press, New YorkJacomy, M., Venturini, T., Heymann, S., Bastian, M., ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software (2014) Plos ONE, 9 (6), pp. 1-12. , https://doi.org/10.1371/journal.pone.0098679Leisch, F., A toolbox for K-centroids cluster analysis (2006) Comput. Stat. Data Anal., 51 (2), pp. 526-544. , https://doi.org/10.1016/j.csda.2005.10.006Leisch, F., Neighborhood graphs, stripes and shadow plots for cluster visualization (2010) Stat. Comput., 20 (4), pp. 457-469. , https://doi.org/10.1007/s11222-009-9137-8van der Maaten, L., Accelerating t-SNE using tree-based algorithms (2014) J. Mach. Learn. Res., 15, pp. 3221-3245. , http://jmlr.org/papers/v15/vandermaaten14a.htmlvan der Maaten, L., Hinton, G., Visualizing high-dimensional data using t-SNE (2008) J. Mach. Learn. Res., 9, pp. 2579-2605. , http://www.jmlr.org/papers/v9/vandermaaten08a.htmlMartin, S., Brown, W.M., Klavans, R., Boyack, K.W., OpenOrd: An open-source toolbox for large graph layout (2011) Proceedings of SPIE, P, 7868. , https://doi.org/10.1117/12.871402, JanuaryMetsalu, T., Vilo, J., ClustVis: A web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap (2015) Nucleic Acids Res, 43 (W1), pp. W566-W570. , https://doi.org/10.1093/nar/gkv468Newman, M.E.J., The structure and function of complex networks (2003) SIAM Rev, 45 (2), pp. 167-256. , https://doi.org/10.1137/S003614450342480Pison, G., Struyf, A., Rousseeuw, P.J., Displaying a clustering with CLUSPLOT (1999) Comput. Stat. Data Anal., 30 (4), pp. 381-392. , https://doi.org/10.1016/S0167-9473(98)00102-9Sato-Ilic, M., Ilic, P., Visualization of fuzzy clustering result in metric space (2016) Proc. Comput. Sci., 96, pp. 1666-1675. , https://doi.org/10.1016/j.procs.2016.08.214Serra, A., Galdi, P., Tagliaferri, R., Machine learning for bioinformatics and neu-roimaging (2018) Wiley Interdisc. Rev.: Data Min. Knowl. Discov, 8 (5), pp. 1-33. , https://doi.org/10.1002/widm.1248Sharko, J., Grinstein, G., Visualizing fuzzy clusters using RadViz (2009) Proceedings of International Conference Information Visualisation, pp. 307-316. , https://doi.org/10.1109/IV.2009.74, ppWang, K.J., Yan, X.H., Chen, L.F., Geometric double-entity model for recognizing far-near relations of clusters (2011) Sci. China Inf. Sci., 54 (10), pp. 2040-2050. , https://doi.org/10.1007/s11432-011-4386-5Wang, W., Zhang, Y., On fuzzy cluster validity indices (2007) Fuzzy Sets Syst, 158 (19), pp. 2095-2117. , https://doi.org/10.1016/j.fss.2007.03.004Xu, R., Wunsch, D., Survey of clustering algorithms (2005) IEEE Trans. Neural Netw., 16 (3), pp. 645-678. , https://doi.org/10.1109/TNN.2005.845141Xu, R., Wunsch, D.C., Clustering algorithms in biomedical research: A review (2010) IEEE Rev. Biomed. Eng., 3, pp. 120-154. , https://doi.org/10.1109/RBME.2010.2083647Zhou, F., A radviz-based visualization for understanding fuzzy clustering results (2017) Proceedings of 10Th International Symposium on Visual Information Communication and Interaction, pp. 9-15. , https://doi.org/10.1145/3105971.3105980, pp., ACM, New YorkCommunications in Computer and Information ScienceClustering visualizationFuzzy clusteringHigh-dimensional dataMembership networkCluster analysisComplex networksData visualizationFuzzy clusteringInput output programsLarge datasetVisualizationCluster structureFinancial profilesHard clusteringHigh dimensional dataHigh-dimensionalNon-trivial tasksSimple networksWeighted networksClustering algorithmsMemberships Networks for High-Dimensional Fuzzy Clustering VisualizationConference Paperinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Ariza-Jiménez, L., Mathematical Modeling Research Group, Universidad EAFIT, Medellín, Colombia; Villa, L.F., System Engineering Research Group, ARKADIUS, Universidad de Medellín, Medellín, Colombia; Quintero, O.L., Mathematical Modeling Research Group, Universidad EAFIT, Medellín, Colombiahttp://purl.org/coar/access_right/c_16ecAriza-Jiménez L.Villa L.F.Quintero O.L.11407/5662oai:repository.udem.edu.co:11407/56622020-05-27 15:54:59.631Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

Memberships Networks for High-Dimensional Fuzzy Clustering Visualization

Publicaciones similares