An Extraction and Regularization Approach to Additive Clustering
Abstract Additive clustering provides a conceptually simple similarity model which is, nevertheless, capable of accommodating arbitrary similarity structures. The discrete nature of the clusters, coupled with the general flexibility of the model, however, means that the derivation of additive cluste...
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| Autor*in: |
Lee, Michael D. [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
1999 |
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| Anmerkung: |
© Springer-Verlag New York Inc. 1999 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of classification - Springer-Verlag, 1984, 16(1999), 2 vom: Juli, Seite 255-281 |
|---|---|
| Übergeordnetes Werk: |
volume:16 ; year:1999 ; number:2 ; month:07 ; pages:255-281 |
| Links: |
|---|
| DOI / URN: |
10.1007/s003579900056 |
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OLC2062461127 |
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| 520 | |a Abstract Additive clustering provides a conceptually simple similarity model which is, nevertheless, capable of accommodating arbitrary similarity structures. The discrete nature of the clusters, coupled with the general flexibility of the model, however, means that the derivation of additive clustering models from given similarity data is difficult. After reviewing a number of previously developed algorithms, a new two stage algorithm for generating additive cluster models is developed. In the first stage, an extraction process generates a manageable number of candidate clusters which, in the second stage, are subject to a regularization process. The number of clusters included in the derived model is controlled by a parameter specifying the target level of variance to be accounted for by the final model. Several applications of the proposed algorithm are presented, including three involving previously examined data sets that facilitate an evaluation of performance relative to several other algorithms. It is argued that the proposed algorithm exhibits comparable performance in relation to these previous algorithms, and has the advantage of being developed within a framework that potentially allows the optimization of the tradeoff between goodness-of-fit and model parsimony. | ||
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10.1007/s003579900056 doi (DE-627)OLC2062461127 (DE-He213)s003579900056-p DE-627 ger DE-627 rakwb eng 150 510 600 VZ 24,1 ssgn Lee, Michael D. verfasserin aut An Extraction and Regularization Approach to Additive Clustering 1999 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag New York Inc. 1999 Abstract Additive clustering provides a conceptually simple similarity model which is, nevertheless, capable of accommodating arbitrary similarity structures. The discrete nature of the clusters, coupled with the general flexibility of the model, however, means that the derivation of additive clustering models from given similarity data is difficult. After reviewing a number of previously developed algorithms, a new two stage algorithm for generating additive cluster models is developed. In the first stage, an extraction process generates a manageable number of candidate clusters which, in the second stage, are subject to a regularization process. The number of clusters included in the derived model is controlled by a parameter specifying the target level of variance to be accounted for by the final model. Several applications of the proposed algorithm are presented, including three involving previously examined data sets that facilitate an evaluation of performance relative to several other algorithms. It is argued that the proposed algorithm exhibits comparable performance in relation to these previous algorithms, and has the advantage of being developed within a framework that potentially allows the optimization of the tradeoff between goodness-of-fit and model parsimony. Enthalten in Journal of classification Springer-Verlag, 1984 16(1999), 2 vom: Juli, Seite 255-281 (DE-627)129337323 (DE-600)142885-8 (DE-576)014642832 0176-4268 nnns volume:16 year:1999 number:2 month:07 pages:255-281 https://doi.org/10.1007/s003579900056 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_40 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4103 GBV_ILN_4126 GBV_ILN_4277 GBV_ILN_4310 GBV_ILN_4324 AR 16 1999 2 07 255-281 |
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10.1007/s003579900056 doi (DE-627)OLC2062461127 (DE-He213)s003579900056-p DE-627 ger DE-627 rakwb eng 150 510 600 VZ 24,1 ssgn Lee, Michael D. verfasserin aut An Extraction and Regularization Approach to Additive Clustering 1999 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag New York Inc. 1999 Abstract Additive clustering provides a conceptually simple similarity model which is, nevertheless, capable of accommodating arbitrary similarity structures. The discrete nature of the clusters, coupled with the general flexibility of the model, however, means that the derivation of additive clustering models from given similarity data is difficult. After reviewing a number of previously developed algorithms, a new two stage algorithm for generating additive cluster models is developed. In the first stage, an extraction process generates a manageable number of candidate clusters which, in the second stage, are subject to a regularization process. The number of clusters included in the derived model is controlled by a parameter specifying the target level of variance to be accounted for by the final model. Several applications of the proposed algorithm are presented, including three involving previously examined data sets that facilitate an evaluation of performance relative to several other algorithms. It is argued that the proposed algorithm exhibits comparable performance in relation to these previous algorithms, and has the advantage of being developed within a framework that potentially allows the optimization of the tradeoff between goodness-of-fit and model parsimony. Enthalten in Journal of classification Springer-Verlag, 1984 16(1999), 2 vom: Juli, Seite 255-281 (DE-627)129337323 (DE-600)142885-8 (DE-576)014642832 0176-4268 nnns volume:16 year:1999 number:2 month:07 pages:255-281 https://doi.org/10.1007/s003579900056 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_40 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4103 GBV_ILN_4126 GBV_ILN_4277 GBV_ILN_4310 GBV_ILN_4324 AR 16 1999 2 07 255-281 |
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10.1007/s003579900056 doi (DE-627)OLC2062461127 (DE-He213)s003579900056-p DE-627 ger DE-627 rakwb eng 150 510 600 VZ 24,1 ssgn Lee, Michael D. verfasserin aut An Extraction and Regularization Approach to Additive Clustering 1999 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag New York Inc. 1999 Abstract Additive clustering provides a conceptually simple similarity model which is, nevertheless, capable of accommodating arbitrary similarity structures. The discrete nature of the clusters, coupled with the general flexibility of the model, however, means that the derivation of additive clustering models from given similarity data is difficult. After reviewing a number of previously developed algorithms, a new two stage algorithm for generating additive cluster models is developed. In the first stage, an extraction process generates a manageable number of candidate clusters which, in the second stage, are subject to a regularization process. The number of clusters included in the derived model is controlled by a parameter specifying the target level of variance to be accounted for by the final model. Several applications of the proposed algorithm are presented, including three involving previously examined data sets that facilitate an evaluation of performance relative to several other algorithms. It is argued that the proposed algorithm exhibits comparable performance in relation to these previous algorithms, and has the advantage of being developed within a framework that potentially allows the optimization of the tradeoff between goodness-of-fit and model parsimony. Enthalten in Journal of classification Springer-Verlag, 1984 16(1999), 2 vom: Juli, Seite 255-281 (DE-627)129337323 (DE-600)142885-8 (DE-576)014642832 0176-4268 nnns volume:16 year:1999 number:2 month:07 pages:255-281 https://doi.org/10.1007/s003579900056 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_40 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4103 GBV_ILN_4126 GBV_ILN_4277 GBV_ILN_4310 GBV_ILN_4324 AR 16 1999 2 07 255-281 |
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10.1007/s003579900056 doi (DE-627)OLC2062461127 (DE-He213)s003579900056-p DE-627 ger DE-627 rakwb eng 150 510 600 VZ 24,1 ssgn Lee, Michael D. verfasserin aut An Extraction and Regularization Approach to Additive Clustering 1999 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag New York Inc. 1999 Abstract Additive clustering provides a conceptually simple similarity model which is, nevertheless, capable of accommodating arbitrary similarity structures. The discrete nature of the clusters, coupled with the general flexibility of the model, however, means that the derivation of additive clustering models from given similarity data is difficult. After reviewing a number of previously developed algorithms, a new two stage algorithm for generating additive cluster models is developed. In the first stage, an extraction process generates a manageable number of candidate clusters which, in the second stage, are subject to a regularization process. The number of clusters included in the derived model is controlled by a parameter specifying the target level of variance to be accounted for by the final model. Several applications of the proposed algorithm are presented, including three involving previously examined data sets that facilitate an evaluation of performance relative to several other algorithms. It is argued that the proposed algorithm exhibits comparable performance in relation to these previous algorithms, and has the advantage of being developed within a framework that potentially allows the optimization of the tradeoff between goodness-of-fit and model parsimony. Enthalten in Journal of classification Springer-Verlag, 1984 16(1999), 2 vom: Juli, Seite 255-281 (DE-627)129337323 (DE-600)142885-8 (DE-576)014642832 0176-4268 nnns volume:16 year:1999 number:2 month:07 pages:255-281 https://doi.org/10.1007/s003579900056 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_11 GBV_ILN_40 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2012 GBV_ILN_2018 GBV_ILN_2021 GBV_ILN_4012 GBV_ILN_4103 GBV_ILN_4126 GBV_ILN_4277 GBV_ILN_4310 GBV_ILN_4324 AR 16 1999 2 07 255-281 |
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An Extraction and Regularization Approach to Additive Clustering |
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Abstract Additive clustering provides a conceptually simple similarity model which is, nevertheless, capable of accommodating arbitrary similarity structures. The discrete nature of the clusters, coupled with the general flexibility of the model, however, means that the derivation of additive clustering models from given similarity data is difficult. After reviewing a number of previously developed algorithms, a new two stage algorithm for generating additive cluster models is developed. In the first stage, an extraction process generates a manageable number of candidate clusters which, in the second stage, are subject to a regularization process. The number of clusters included in the derived model is controlled by a parameter specifying the target level of variance to be accounted for by the final model. Several applications of the proposed algorithm are presented, including three involving previously examined data sets that facilitate an evaluation of performance relative to several other algorithms. It is argued that the proposed algorithm exhibits comparable performance in relation to these previous algorithms, and has the advantage of being developed within a framework that potentially allows the optimization of the tradeoff between goodness-of-fit and model parsimony. © Springer-Verlag New York Inc. 1999 |
| abstractGer |
Abstract Additive clustering provides a conceptually simple similarity model which is, nevertheless, capable of accommodating arbitrary similarity structures. The discrete nature of the clusters, coupled with the general flexibility of the model, however, means that the derivation of additive clustering models from given similarity data is difficult. After reviewing a number of previously developed algorithms, a new two stage algorithm for generating additive cluster models is developed. In the first stage, an extraction process generates a manageable number of candidate clusters which, in the second stage, are subject to a regularization process. The number of clusters included in the derived model is controlled by a parameter specifying the target level of variance to be accounted for by the final model. Several applications of the proposed algorithm are presented, including three involving previously examined data sets that facilitate an evaluation of performance relative to several other algorithms. It is argued that the proposed algorithm exhibits comparable performance in relation to these previous algorithms, and has the advantage of being developed within a framework that potentially allows the optimization of the tradeoff between goodness-of-fit and model parsimony. © Springer-Verlag New York Inc. 1999 |
| abstract_unstemmed |
Abstract Additive clustering provides a conceptually simple similarity model which is, nevertheless, capable of accommodating arbitrary similarity structures. The discrete nature of the clusters, coupled with the general flexibility of the model, however, means that the derivation of additive clustering models from given similarity data is difficult. After reviewing a number of previously developed algorithms, a new two stage algorithm for generating additive cluster models is developed. In the first stage, an extraction process generates a manageable number of candidate clusters which, in the second stage, are subject to a regularization process. The number of clusters included in the derived model is controlled by a parameter specifying the target level of variance to be accounted for by the final model. Several applications of the proposed algorithm are presented, including three involving previously examined data sets that facilitate an evaluation of performance relative to several other algorithms. It is argued that the proposed algorithm exhibits comparable performance in relation to these previous algorithms, and has the advantage of being developed within a framework that potentially allows the optimization of the tradeoff between goodness-of-fit and model parsimony. © Springer-Verlag New York Inc. 1999 |
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| container_issue |
2 |
| title_short |
An Extraction and Regularization Approach to Additive Clustering |
| url |
https://doi.org/10.1007/s003579900056 |
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| up_date |
2024-07-03T15:09:14.675Z |
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