Mixture-based clustering for the ordered stereotype model
Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count da...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fernández, D. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Schlagwörter: |
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| Umfang: |
30 |
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| Übergeordnetes Werk: |
Enthalten in: An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center - Phillips, Eileen ELSEVIER, 2014, Amsterdam |
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| Übergeordnetes Werk: |
volume:93 ; year:2016 ; pages:46-75 ; extent:30 |
| Links: |
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| DOI / URN: |
10.1016/j.csda.2014.11.004 |
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ELV035135468 |
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| 520 | |a Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. | ||
| 520 | |a Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. | ||
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10.1016/j.csda.2014.11.004 doi GBVA2016005000015.pica (DE-627)ELV035135468 (ELSEVIER)S0167-9473(14)00317-X DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Fernández, D. verfasserin aut Mixture-based clustering for the ordered stereotype model 2016transfer abstract 30 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. Cluster analysis Elsevier Likert scale Elsevier Dimension reduction Elsevier Biclustering Elsevier Fuzzy clustering Elsevier Stereotype model Elsevier Finite mixture model Elsevier EM-algorithm Elsevier Ordinal data Elsevier Arnold, R. oth Pledger, S. oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:93 year:2016 pages:46-75 extent:30 https://doi.org/10.1016/j.csda.2014.11.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 93 2016 46-75 30 045F 004 |
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10.1016/j.csda.2014.11.004 doi GBVA2016005000015.pica (DE-627)ELV035135468 (ELSEVIER)S0167-9473(14)00317-X DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Fernández, D. verfasserin aut Mixture-based clustering for the ordered stereotype model 2016transfer abstract 30 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. Cluster analysis Elsevier Likert scale Elsevier Dimension reduction Elsevier Biclustering Elsevier Fuzzy clustering Elsevier Stereotype model Elsevier Finite mixture model Elsevier EM-algorithm Elsevier Ordinal data Elsevier Arnold, R. oth Pledger, S. oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:93 year:2016 pages:46-75 extent:30 https://doi.org/10.1016/j.csda.2014.11.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 93 2016 46-75 30 045F 004 |
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10.1016/j.csda.2014.11.004 doi GBVA2016005000015.pica (DE-627)ELV035135468 (ELSEVIER)S0167-9473(14)00317-X DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Fernández, D. verfasserin aut Mixture-based clustering for the ordered stereotype model 2016transfer abstract 30 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. Cluster analysis Elsevier Likert scale Elsevier Dimension reduction Elsevier Biclustering Elsevier Fuzzy clustering Elsevier Stereotype model Elsevier Finite mixture model Elsevier EM-algorithm Elsevier Ordinal data Elsevier Arnold, R. oth Pledger, S. oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:93 year:2016 pages:46-75 extent:30 https://doi.org/10.1016/j.csda.2014.11.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 93 2016 46-75 30 045F 004 |
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10.1016/j.csda.2014.11.004 doi GBVA2016005000015.pica (DE-627)ELV035135468 (ELSEVIER)S0167-9473(14)00317-X DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Fernández, D. verfasserin aut Mixture-based clustering for the ordered stereotype model 2016transfer abstract 30 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. Cluster analysis Elsevier Likert scale Elsevier Dimension reduction Elsevier Biclustering Elsevier Fuzzy clustering Elsevier Stereotype model Elsevier Finite mixture model Elsevier EM-algorithm Elsevier Ordinal data Elsevier Arnold, R. oth Pledger, S. oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:93 year:2016 pages:46-75 extent:30 https://doi.org/10.1016/j.csda.2014.11.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 93 2016 46-75 30 045F 004 |
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10.1016/j.csda.2014.11.004 doi GBVA2016005000015.pica (DE-627)ELV035135468 (ELSEVIER)S0167-9473(14)00317-X DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 540 VZ 35.18 bkl Fernández, D. verfasserin aut Mixture-based clustering for the ordered stereotype model 2016transfer abstract 30 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. Cluster analysis Elsevier Likert scale Elsevier Dimension reduction Elsevier Biclustering Elsevier Fuzzy clustering Elsevier Stereotype model Elsevier Finite mixture model Elsevier EM-algorithm Elsevier Ordinal data Elsevier Arnold, R. oth Pledger, S. oth Enthalten in Elsevier Science Phillips, Eileen ELSEVIER An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center 2014 Amsterdam (DE-627)ELV022563539 volume:93 year:2016 pages:46-75 extent:30 https://doi.org/10.1016/j.csda.2014.11.004 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_130 35.18 Kolloidchemie Grenzflächenchemie VZ AR 93 2016 46-75 30 045F 004 |
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ddc 004 ddc 610 ddc 540 bkl 35.18 Elsevier Cluster analysis Elsevier Likert scale Elsevier Dimension reduction Elsevier Biclustering Elsevier Fuzzy clustering Elsevier Stereotype model Elsevier Finite mixture model Elsevier EM-algorithm Elsevier Ordinal data |
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An Orthopaedic Pre-operative Skin Decolonization Protocol Process Improvement Project at an Academic Medical Center |
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mixture-based clustering for the ordered stereotype model |
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Mixture-based clustering for the ordered stereotype model |
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Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. |
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Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. |
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Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks has been developed. This is extended and establishes likelihood-based multivariate methods for a data matrix with ordinal data which applies fuzzy clustering via finite mixtures to the ordered stereotype model. Model-fitting is performed using the expectation–maximization (EM) algorithm, and a fuzzy allocation of rows, columns, and rows and columns simultaneously to corresponding clusters is obtained. A simulation study is presented which includes a variety of scenarios in order to test the reliability of the proposed model. Finally, the results of the application of the model in two real data sets are shown. |
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