Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering

Abstract For multivariate categorical data, it is important to detect both clustering structures and low dimensions such that clusters are discriminated. This is because it is easy to interpret the features of clusters through the estimated low dimensions. It is sure that these existing methods for...
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Gespeichert in:

Autor*in:	Tanioka, Kensuke [verfasserIn] Yadohisa, Hiroshi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Dimensional reduction Perfect simple structure Categorical -mode clustering

Anmerkung:	© The Classification Society 2019

Übergeordnetes Werk:	Enthalten in: Journal of classification - New York, NY : Springer, 1984, 36(2019), 1 vom: 29. März, Seite 73-93
Übergeordnetes Werk:	volume:36 ; year:2019 ; number:1 ; day:29 ; month:03 ; pages:73-93

Links:	Volltext

DOI / URN:	10.1007/s00357-018-9284-8

Katalog-ID:	SPR004454235

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520			\|a Abstract For multivariate categorical data, it is important to detect both clustering structures and low dimensions such that clusters are discriminated. This is because it is easy to interpret the features of clusters through the estimated low dimensions. It is sure that these existing methods for dimensional reduction clustering are useful to achieve such purpose; however, the interpretation sometimes becomes complicated due to the sign of the estimated parameters. Thus, we propose new dimensional reduction clustering with non-negativity constraints for all parameters. The proposed method has several advantages. First, when the features of clusters are interpreted, it is easier to interpret the clusters since effects of sign should not be considered. In addition, from the non-negativity and orthogonality constraints, the estimated components become perfect simple structure, which is interpretable descriptions. Second, we showed that the clustering results are not inferior to these existing methods through the simulations, although the constraints for the proposed method are strong.
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650		4	\|a -mode clustering \|7 (dpeaa)DE-He213
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912			\|a GBV_ILN_293
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912			\|a GBV_ILN_602
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publishDate	2019
allfields	10.1007/s00357-018-9284-8 doi (DE-627)SPR004454235 (SPR)s00357-018-9284-8-e DE-627 ger DE-627 rakwb eng Tanioka, Kensuke verfasserin aut Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Classification Society 2019 Abstract For multivariate categorical data, it is important to detect both clustering structures and low dimensions such that clusters are discriminated. This is because it is easy to interpret the features of clusters through the estimated low dimensions. It is sure that these existing methods for dimensional reduction clustering are useful to achieve such purpose; however, the interpretation sometimes becomes complicated due to the sign of the estimated parameters. Thus, we propose new dimensional reduction clustering with non-negativity constraints for all parameters. The proposed method has several advantages. First, when the features of clusters are interpreted, it is easier to interpret the clusters since effects of sign should not be considered. In addition, from the non-negativity and orthogonality constraints, the estimated components become perfect simple structure, which is interpretable descriptions. Second, we showed that the clustering results are not inferior to these existing methods through the simulations, although the constraints for the proposed method are strong. Dimensional reduction (dpeaa)DE-He213 Perfect simple structure (dpeaa)DE-He213 Categorical (dpeaa)DE-He213 -mode clustering (dpeaa)DE-He213 Yadohisa, Hiroshi aut Enthalten in Journal of classification New York, NY : Springer, 1984 36(2019), 1 vom: 29. März, Seite 73-93 (DE-627)253769558 (DE-600)1459289-7 1432-1343 nnns volume:36 year:2019 number:1 day:29 month:03 pages:73-93 https://dx.doi.org/10.1007/s00357-018-9284-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2019 1 29 03 73-93
spelling	10.1007/s00357-018-9284-8 doi (DE-627)SPR004454235 (SPR)s00357-018-9284-8-e DE-627 ger DE-627 rakwb eng Tanioka, Kensuke verfasserin aut Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Classification Society 2019 Abstract For multivariate categorical data, it is important to detect both clustering structures and low dimensions such that clusters are discriminated. This is because it is easy to interpret the features of clusters through the estimated low dimensions. It is sure that these existing methods for dimensional reduction clustering are useful to achieve such purpose; however, the interpretation sometimes becomes complicated due to the sign of the estimated parameters. Thus, we propose new dimensional reduction clustering with non-negativity constraints for all parameters. The proposed method has several advantages. First, when the features of clusters are interpreted, it is easier to interpret the clusters since effects of sign should not be considered. In addition, from the non-negativity and orthogonality constraints, the estimated components become perfect simple structure, which is interpretable descriptions. Second, we showed that the clustering results are not inferior to these existing methods through the simulations, although the constraints for the proposed method are strong. Dimensional reduction (dpeaa)DE-He213 Perfect simple structure (dpeaa)DE-He213 Categorical (dpeaa)DE-He213 -mode clustering (dpeaa)DE-He213 Yadohisa, Hiroshi aut Enthalten in Journal of classification New York, NY : Springer, 1984 36(2019), 1 vom: 29. März, Seite 73-93 (DE-627)253769558 (DE-600)1459289-7 1432-1343 nnns volume:36 year:2019 number:1 day:29 month:03 pages:73-93 https://dx.doi.org/10.1007/s00357-018-9284-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2019 1 29 03 73-93
allfields_unstemmed	10.1007/s00357-018-9284-8 doi (DE-627)SPR004454235 (SPR)s00357-018-9284-8-e DE-627 ger DE-627 rakwb eng Tanioka, Kensuke verfasserin aut Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Classification Society 2019 Abstract For multivariate categorical data, it is important to detect both clustering structures and low dimensions such that clusters are discriminated. This is because it is easy to interpret the features of clusters through the estimated low dimensions. It is sure that these existing methods for dimensional reduction clustering are useful to achieve such purpose; however, the interpretation sometimes becomes complicated due to the sign of the estimated parameters. Thus, we propose new dimensional reduction clustering with non-negativity constraints for all parameters. The proposed method has several advantages. First, when the features of clusters are interpreted, it is easier to interpret the clusters since effects of sign should not be considered. In addition, from the non-negativity and orthogonality constraints, the estimated components become perfect simple structure, which is interpretable descriptions. Second, we showed that the clustering results are not inferior to these existing methods through the simulations, although the constraints for the proposed method are strong. Dimensional reduction (dpeaa)DE-He213 Perfect simple structure (dpeaa)DE-He213 Categorical (dpeaa)DE-He213 -mode clustering (dpeaa)DE-He213 Yadohisa, Hiroshi aut Enthalten in Journal of classification New York, NY : Springer, 1984 36(2019), 1 vom: 29. März, Seite 73-93 (DE-627)253769558 (DE-600)1459289-7 1432-1343 nnns volume:36 year:2019 number:1 day:29 month:03 pages:73-93 https://dx.doi.org/10.1007/s00357-018-9284-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2019 1 29 03 73-93
allfieldsGer	10.1007/s00357-018-9284-8 doi (DE-627)SPR004454235 (SPR)s00357-018-9284-8-e DE-627 ger DE-627 rakwb eng Tanioka, Kensuke verfasserin aut Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Classification Society 2019 Abstract For multivariate categorical data, it is important to detect both clustering structures and low dimensions such that clusters are discriminated. This is because it is easy to interpret the features of clusters through the estimated low dimensions. It is sure that these existing methods for dimensional reduction clustering are useful to achieve such purpose; however, the interpretation sometimes becomes complicated due to the sign of the estimated parameters. Thus, we propose new dimensional reduction clustering with non-negativity constraints for all parameters. The proposed method has several advantages. First, when the features of clusters are interpreted, it is easier to interpret the clusters since effects of sign should not be considered. In addition, from the non-negativity and orthogonality constraints, the estimated components become perfect simple structure, which is interpretable descriptions. Second, we showed that the clustering results are not inferior to these existing methods through the simulations, although the constraints for the proposed method are strong. Dimensional reduction (dpeaa)DE-He213 Perfect simple structure (dpeaa)DE-He213 Categorical (dpeaa)DE-He213 -mode clustering (dpeaa)DE-He213 Yadohisa, Hiroshi aut Enthalten in Journal of classification New York, NY : Springer, 1984 36(2019), 1 vom: 29. März, Seite 73-93 (DE-627)253769558 (DE-600)1459289-7 1432-1343 nnns volume:36 year:2019 number:1 day:29 month:03 pages:73-93 https://dx.doi.org/10.1007/s00357-018-9284-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2019 1 29 03 73-93
allfieldsSound	10.1007/s00357-018-9284-8 doi (DE-627)SPR004454235 (SPR)s00357-018-9284-8-e DE-627 ger DE-627 rakwb eng Tanioka, Kensuke verfasserin aut Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Classification Society 2019 Abstract For multivariate categorical data, it is important to detect both clustering structures and low dimensions such that clusters are discriminated. This is because it is easy to interpret the features of clusters through the estimated low dimensions. It is sure that these existing methods for dimensional reduction clustering are useful to achieve such purpose; however, the interpretation sometimes becomes complicated due to the sign of the estimated parameters. Thus, we propose new dimensional reduction clustering with non-negativity constraints for all parameters. The proposed method has several advantages. First, when the features of clusters are interpreted, it is easier to interpret the clusters since effects of sign should not be considered. In addition, from the non-negativity and orthogonality constraints, the estimated components become perfect simple structure, which is interpretable descriptions. Second, we showed that the clustering results are not inferior to these existing methods through the simulations, although the constraints for the proposed method are strong. Dimensional reduction (dpeaa)DE-He213 Perfect simple structure (dpeaa)DE-He213 Categorical (dpeaa)DE-He213 -mode clustering (dpeaa)DE-He213 Yadohisa, Hiroshi aut Enthalten in Journal of classification New York, NY : Springer, 1984 36(2019), 1 vom: 29. März, Seite 73-93 (DE-627)253769558 (DE-600)1459289-7 1432-1343 nnns volume:36 year:2019 number:1 day:29 month:03 pages:73-93 https://dx.doi.org/10.1007/s00357-018-9284-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 36 2019 1 29 03 73-93
language	English
source	Enthalten in Journal of classification 36(2019), 1 vom: 29. März, Seite 73-93 volume:36 year:2019 number:1 day:29 month:03 pages:73-93
sourceStr	Enthalten in Journal of classification 36(2019), 1 vom: 29. März, Seite 73-93 volume:36 year:2019 number:1 day:29 month:03 pages:73-93
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Dimensional reduction Perfect simple structure Categorical -mode clustering
isfreeaccess_bool	false
container_title	Journal of classification
authorswithroles_txt_mv	Tanioka, Kensuke @@aut@@ Yadohisa, Hiroshi @@aut@@
publishDateDaySort_date	2019-03-29T00:00:00Z
hierarchy_top_id	253769558
id	SPR004454235
language_de	englisch
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author	Tanioka, Kensuke
spellingShingle	Tanioka, Kensuke misc Dimensional reduction misc Perfect simple structure misc Categorical misc -mode clustering Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering
authorStr	Tanioka, Kensuke
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topic_title	Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering Dimensional reduction (dpeaa)DE-He213 Perfect simple structure (dpeaa)DE-He213 Categorical (dpeaa)DE-He213 -mode clustering (dpeaa)DE-He213
topic	misc Dimensional reduction misc Perfect simple structure misc Categorical misc -mode clustering
topic_unstemmed	misc Dimensional reduction misc Perfect simple structure misc Categorical misc -mode clustering
topic_browse	misc Dimensional reduction misc Perfect simple structure misc Categorical misc -mode clustering
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title	Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering
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title_full	Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering
author_sort	Tanioka, Kensuke
journal	Journal of classification
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author_browse	Tanioka, Kensuke Yadohisa, Hiroshi
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author-letter	Tanioka, Kensuke
doi_str_mv	10.1007/s00357-018-9284-8
title_sort	simultaneous method of orthogonal non-metric non-negative matrix factorization and constrained non-hierarchical clustering
title_auth	Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering
abstract	Abstract For multivariate categorical data, it is important to detect both clustering structures and low dimensions such that clusters are discriminated. This is because it is easy to interpret the features of clusters through the estimated low dimensions. It is sure that these existing methods for dimensional reduction clustering are useful to achieve such purpose; however, the interpretation sometimes becomes complicated due to the sign of the estimated parameters. Thus, we propose new dimensional reduction clustering with non-negativity constraints for all parameters. The proposed method has several advantages. First, when the features of clusters are interpreted, it is easier to interpret the clusters since effects of sign should not be considered. In addition, from the non-negativity and orthogonality constraints, the estimated components become perfect simple structure, which is interpretable descriptions. Second, we showed that the clustering results are not inferior to these existing methods through the simulations, although the constraints for the proposed method are strong. © The Classification Society 2019
abstractGer	Abstract For multivariate categorical data, it is important to detect both clustering structures and low dimensions such that clusters are discriminated. This is because it is easy to interpret the features of clusters through the estimated low dimensions. It is sure that these existing methods for dimensional reduction clustering are useful to achieve such purpose; however, the interpretation sometimes becomes complicated due to the sign of the estimated parameters. Thus, we propose new dimensional reduction clustering with non-negativity constraints for all parameters. The proposed method has several advantages. First, when the features of clusters are interpreted, it is easier to interpret the clusters since effects of sign should not be considered. In addition, from the non-negativity and orthogonality constraints, the estimated components become perfect simple structure, which is interpretable descriptions. Second, we showed that the clustering results are not inferior to these existing methods through the simulations, although the constraints for the proposed method are strong. © The Classification Society 2019
abstract_unstemmed	Abstract For multivariate categorical data, it is important to detect both clustering structures and low dimensions such that clusters are discriminated. This is because it is easy to interpret the features of clusters through the estimated low dimensions. It is sure that these existing methods for dimensional reduction clustering are useful to achieve such purpose; however, the interpretation sometimes becomes complicated due to the sign of the estimated parameters. Thus, we propose new dimensional reduction clustering with non-negativity constraints for all parameters. The proposed method has several advantages. First, when the features of clusters are interpreted, it is easier to interpret the clusters since effects of sign should not be considered. In addition, from the non-negativity and orthogonality constraints, the estimated components become perfect simple structure, which is interpretable descriptions. Second, we showed that the clustering results are not inferior to these existing methods through the simulations, although the constraints for the proposed method are strong. © The Classification Society 2019
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title_short	Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering
url	https://dx.doi.org/10.1007/s00357-018-9284-8
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author2	Yadohisa, Hiroshi
author2Str	Yadohisa, Hiroshi
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doi_str	10.1007/s00357-018-9284-8
up_date	2024-07-04T01:09:52.705Z
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Simultaneous Method of Orthogonal Non-metric Non-negative Matrix Factorization and Constrained Non-hierarchical Clustering

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