Perron cluster analysis and its connection to graph partitioning for noisy data:
Abstract: "The problem of clustering data can be formulated as a graph partitioning problem. Spectral methods for obtaining optimal solutions have reveceived [sic] a lot of attention recently. We describe Perron Cluster Cluster Analysis (PCCA) and, for the first time, establish a connection to...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin
Konrad-Zuse-Zentrum für Informationstechnik
2004
|
| Schriftenreihe: | ZIB-Report
2004,39 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "The problem of clustering data can be formulated as a graph partitioning problem. Spectral methods for obtaining optimal solutions have reveceived [sic] a lot of attention recently. We describe Perron Cluster Cluster Analysis (PCCA) and, for the first time, establish a connection to spectral graph partitioning. We show that in our approach a clustering can be efficiently computed using a simple linear map of the eigenvector data. To deal with the prevalent problem of noisy and possibly overlapping data we introduce the minChi indicator which helps in selecting the number of clusters and confirming the existence of a partition of the data. This gives a non-probabilistic alternative to statistical mixture-models. We close with showing favorable results on the analysis of gene expression data for two different cancer types." |
| Beschreibung: | 20 S. Ill., graph. Darst. |
Internformat
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| 245 | 1 | 0 | |a Perron cluster analysis and its connection to graph partitioning for noisy data |c Marcus Weber, Wasinee Rungsarityotin, Alexander Schliep |
| 264 | 1 | |a Berlin |b Konrad-Zuse-Zentrum für Informationstechnik |c 2004 | |
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| 520 | 3 | |a Abstract: "The problem of clustering data can be formulated as a graph partitioning problem. Spectral methods for obtaining optimal solutions have reveceived [sic] a lot of attention recently. We describe Perron Cluster Cluster Analysis (PCCA) and, for the first time, establish a connection to spectral graph partitioning. We show that in our approach a clustering can be efficiently computed using a simple linear map of the eigenvector data. To deal with the prevalent problem of noisy and possibly overlapping data we introduce the minChi indicator which helps in selecting the number of clusters and confirming the existence of a partition of the data. This gives a non-probabilistic alternative to statistical mixture-models. We close with showing favorable results on the analysis of gene expression data for two different cancer types." | |
| 650 | 4 | |a Cluster analysis | |
| 650 | 4 | |a Graph theory | |
| 700 | 1 | |a Rungsarityotin, Wasinee |e Verfasser |0 (DE-588)129644439 |4 aut | |
| 700 | 1 | |a Schliep, Alexander |e Verfasser |4 aut | |
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Datensatz im Suchindex
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| any_adam_object | |
| author | Weber, Marcus 1972- Rungsarityotin, Wasinee Schliep, Alexander |
| author_GND | (DE-588)132289881 (DE-588)129644439 |
| author_facet | Weber, Marcus 1972- Rungsarityotin, Wasinee Schliep, Alexander |
| author_role | aut aut aut |
| author_sort | Weber, Marcus 1972- |
| author_variant | m w mw w r wr a s as |
| building | Verbundindex |
| bvnumber | BV019643106 |
| classification_rvk | SS 4779 |
| ctrlnum | (OCoLC)60248901 (DE-599)BVBBV019643106 |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV019643106 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T20:02:00Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-012971938 |
| oclc_num | 60248901 |
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| owner_facet | DE-703 DE-188 |
| physical | 20 S. Ill., graph. Darst. |
| publishDate | 2004 |
| publishDateSearch | 2004 |
| publishDateSort | 2004 |
| publisher | Konrad-Zuse-Zentrum für Informationstechnik |
| record_format | marc |
| series | ZIB-Report |
| series2 | ZIB-Report |
| spelling | Weber, Marcus 1972- Verfasser (DE-588)132289881 aut Perron cluster analysis and its connection to graph partitioning for noisy data Marcus Weber, Wasinee Rungsarityotin, Alexander Schliep Berlin Konrad-Zuse-Zentrum für Informationstechnik 2004 20 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier ZIB-Report 2004,39 Abstract: "The problem of clustering data can be formulated as a graph partitioning problem. Spectral methods for obtaining optimal solutions have reveceived [sic] a lot of attention recently. We describe Perron Cluster Cluster Analysis (PCCA) and, for the first time, establish a connection to spectral graph partitioning. We show that in our approach a clustering can be efficiently computed using a simple linear map of the eigenvector data. To deal with the prevalent problem of noisy and possibly overlapping data we introduce the minChi indicator which helps in selecting the number of clusters and confirming the existence of a partition of the data. This gives a non-probabilistic alternative to statistical mixture-models. We close with showing favorable results on the analysis of gene expression data for two different cancer types." Cluster analysis Graph theory Rungsarityotin, Wasinee Verfasser (DE-588)129644439 aut Schliep, Alexander Verfasser aut ZIB-Report 2004,39 (DE-604)BV013191727 2004,39 |
| spellingShingle | Weber, Marcus 1972- Rungsarityotin, Wasinee Schliep, Alexander Perron cluster analysis and its connection to graph partitioning for noisy data ZIB-Report Cluster analysis Graph theory |
| title | Perron cluster analysis and its connection to graph partitioning for noisy data |
| title_auth | Perron cluster analysis and its connection to graph partitioning for noisy data |
| title_exact_search | Perron cluster analysis and its connection to graph partitioning for noisy data |
| title_full | Perron cluster analysis and its connection to graph partitioning for noisy data Marcus Weber, Wasinee Rungsarityotin, Alexander Schliep |
| title_fullStr | Perron cluster analysis and its connection to graph partitioning for noisy data Marcus Weber, Wasinee Rungsarityotin, Alexander Schliep |
| title_full_unstemmed | Perron cluster analysis and its connection to graph partitioning for noisy data Marcus Weber, Wasinee Rungsarityotin, Alexander Schliep |
| title_short | Perron cluster analysis and its connection to graph partitioning for noisy data |
| title_sort | perron cluster analysis and its connection to graph partitioning for noisy data |
| topic | Cluster analysis Graph theory |
| topic_facet | Cluster analysis Graph theory |
| volume_link | (DE-604)BV013191727 |
| work_keys_str_mv | AT webermarcus perronclusteranalysisanditsconnectiontographpartitioningfornoisydata AT rungsarityotinwasinee perronclusteranalysisanditsconnectiontographpartitioningfornoisydata AT schliepalexander perronclusteranalysisanditsconnectiontographpartitioningfornoisydata |