Estimating the Cluster Tree of a Density by Analyzing the Minimal Spanning Tree of a Sample
runt pruning , a new clustering method that attempts to find modes of a density by analyzing the minimal spanning tree of a sample. The method exploits the connection between the minimal spanning tree and nearest neighbor density (e.g. normal mixture) or about the geometric shapes of the clusters, a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Stuetzle, Werner [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2003 |
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| Schlagwörter: |
|---|
| Anmerkung: |
© Springer-Verlag New York Inc. 2003 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Journal of classification - Springer-Verlag, 1984, 20(2003), 1 vom: Mai, Seite 025-047 |
|---|---|
| Übergeordnetes Werk: |
volume:20 ; year:2003 ; number:1 ; month:05 ; pages:025-047 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00357-003-0004-6 |
|---|
| Katalog-ID: |
OLC2062461860 |
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runt pruning , a new clustering method that attempts to find modes of a density by analyzing the minimal spanning tree of a sample. The method exploits the connection between the minimal spanning tree and nearest neighbor density (e.g. normal mixture) or about the geometric shapes of the clusters, and is computationally feasible for large data sets. © Springer-Verlag New York Inc. 2003 |
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runt pruning , a new clustering method that attempts to find modes of a density by analyzing the minimal spanning tree of a sample. The method exploits the connection between the minimal spanning tree and nearest neighbor density (e.g. normal mixture) or about the geometric shapes of the clusters, and is computationally feasible for large data sets. © Springer-Verlag New York Inc. 2003 |
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runt pruning , a new clustering method that attempts to find modes of a density by analyzing the minimal spanning tree of a sample. The method exploits the connection between the minimal spanning tree and nearest neighbor density (e.g. normal mixture) or about the geometric shapes of the clusters, and is computationally feasible for large data sets. © Springer-Verlag New York Inc. 2003 |
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