Improved Perron cluster analysis:
Abstract: "The problem of clustering data can often be transformed into the problem of finding a hidden block diagonal structure in a stochastic matrix. Deuflhard et al. [9] have proposed an algorithm that states the number k of clusters and uses the sign structure of k eigenvectors of the stoc...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin
Konrad-Zuse-Zentrum für Informationstechnik
2003
|
| Schriftenreihe: | ZIB-Report
2003,04 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "The problem of clustering data can often be transformed into the problem of finding a hidden block diagonal structure in a stochastic matrix. Deuflhard et al. [9] have proposed an algorithm that states the number k of clusters and uses the sign structure of k eigenvectors of the stochastic matrix to solve the cluster problem. Recently Weber and Galliat [8] discovered that this system of eigenvectors can easily be transformed into a system of k membership functions or soft characteristic functions describing the clusters. In this article we explain the corresponding cluster algorithm and point out the underlying theory. By means of numerical examples we explain how the grade of membership can be interpreted." |
| Beschreibung: | 9 S. 1 Ill., graph. Darst. |
Internformat
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| 520 | 3 | |a Abstract: "The problem of clustering data can often be transformed into the problem of finding a hidden block diagonal structure in a stochastic matrix. Deuflhard et al. [9] have proposed an algorithm that states the number k of clusters and uses the sign structure of k eigenvectors of the stochastic matrix to solve the cluster problem. Recently Weber and Galliat [8] discovered that this system of eigenvectors can easily be transformed into a system of k membership functions or soft characteristic functions describing the clusters. In this article we explain the corresponding cluster algorithm and point out the underlying theory. By means of numerical examples we explain how the grade of membership can be interpreted." | |
| 650 | 4 | |a Cluster analysis | |
| 650 | 4 | |a Stochastic matrices | |
| 830 | 0 | |a ZIB-Report |v 2003,04 |w (DE-604)BV013191727 |9 2003,04 | |
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Datensatz im Suchindex
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| bvnumber | BV023035416 |
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| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Book |
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| id | DE-604.BV023035416 |
| illustrated | Illustrated |
| index_date | 2024-07-02T19:18:42Z |
| indexdate | 2024-07-09T21:09:30Z |
| institution | BVB |
| language | English |
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| physical | 9 S. 1 Ill., graph. Darst. |
| publishDate | 2003 |
| publishDateSearch | 2003 |
| publishDateSort | 2003 |
| publisher | Konrad-Zuse-Zentrum für Informationstechnik |
| record_format | marc |
| series | ZIB-Report |
| series2 | ZIB-Report |
| spelling | Weber, Marcus 1972- Verfasser (DE-588)132289881 aut Improved Perron cluster analysis Marcus Weber Berlin Konrad-Zuse-Zentrum für Informationstechnik 2003 9 S. 1 Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier ZIB-Report 2003,04 Abstract: "The problem of clustering data can often be transformed into the problem of finding a hidden block diagonal structure in a stochastic matrix. Deuflhard et al. [9] have proposed an algorithm that states the number k of clusters and uses the sign structure of k eigenvectors of the stochastic matrix to solve the cluster problem. Recently Weber and Galliat [8] discovered that this system of eigenvectors can easily be transformed into a system of k membership functions or soft characteristic functions describing the clusters. In this article we explain the corresponding cluster algorithm and point out the underlying theory. By means of numerical examples we explain how the grade of membership can be interpreted." Cluster analysis Stochastic matrices ZIB-Report 2003,04 (DE-604)BV013191727 2003,04 |
| spellingShingle | Weber, Marcus 1972- Improved Perron cluster analysis ZIB-Report Cluster analysis Stochastic matrices |
| title | Improved Perron cluster analysis |
| title_auth | Improved Perron cluster analysis |
| title_exact_search | Improved Perron cluster analysis |
| title_exact_search_txtP | Improved Perron cluster analysis |
| title_full | Improved Perron cluster analysis Marcus Weber |
| title_fullStr | Improved Perron cluster analysis Marcus Weber |
| title_full_unstemmed | Improved Perron cluster analysis Marcus Weber |
| title_short | Improved Perron cluster analysis |
| title_sort | improved perron cluster analysis |
| topic | Cluster analysis Stochastic matrices |
| topic_facet | Cluster analysis Stochastic matrices |
| volume_link | (DE-604)BV013191727 |
| work_keys_str_mv | AT webermarcus improvedperronclusteranalysis |