| <!DOCTYPE html><html><head><title>R: PowerIterationClustering</title> |
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| <table style="width: 100%;"><tr><td>spark.assignClusters {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table> |
| |
| <h2>PowerIterationClustering</h2> |
| |
| <h3>Description</h3> |
| |
| <p>A scalable graph clustering algorithm. Users can call <code>spark.assignClusters</code> to |
| return a cluster assignment for each input vertex. |
| Run the PIC algorithm and returns a cluster assignment for each input vertex. |
| </p> |
| |
| |
| <h3>Usage</h3> |
| |
| <pre><code class='language-R'>spark.assignClusters(data, ...) |
| |
| ## S4 method for signature 'SparkDataFrame' |
| spark.assignClusters( |
| data, |
| k = 2L, |
| initMode = c("random", "degree"), |
| maxIter = 20L, |
| sourceCol = "src", |
| destinationCol = "dst", |
| weightCol = NULL |
| ) |
| </code></pre> |
| |
| |
| <h3>Arguments</h3> |
| |
| <table> |
| <tr style="vertical-align: top;"><td><code>data</code></td> |
| <td> |
| <p>a SparkDataFrame.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>...</code></td> |
| <td> |
| <p>additional argument(s) passed to the method.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>k</code></td> |
| <td> |
| <p>the number of clusters to create.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>initMode</code></td> |
| <td> |
| <p>the initialization algorithm; "random" or "degree"</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>maxIter</code></td> |
| <td> |
| <p>the maximum number of iterations.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>sourceCol</code></td> |
| <td> |
| <p>the name of the input column for source vertex IDs.</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>destinationCol</code></td> |
| <td> |
| <p>the name of the input column for destination vertex IDs</p> |
| </td></tr> |
| <tr style="vertical-align: top;"><td><code>weightCol</code></td> |
| <td> |
| <p>weight column name. If this is not set or <code>NULL</code>, |
| we treat all instance weights as 1.0.</p> |
| </td></tr> |
| </table> |
| |
| |
| <h3>Value</h3> |
| |
| <p>A dataset that contains columns of vertex id and the corresponding cluster for the id. |
| The schema of it will be: <code>id: integer</code>, <code>cluster: integer</code> |
| </p> |
| |
| |
| <h3>Note</h3> |
| |
| <p>spark.assignClusters(SparkDataFrame) since 3.0.0 |
| </p> |
| |
| |
| <h3>Examples</h3> |
| |
| <pre><code class="r">## Not run: |
| ##D df <- createDataFrame(list(list(0L, 1L, 1.0), list(0L, 2L, 1.0), |
| ##D list(1L, 2L, 1.0), list(3L, 4L, 1.0), |
| ##D list(4L, 0L, 0.1)), |
| ##D schema = c("src", "dst", "weight")) |
| ##D clusters <- spark.assignClusters(df, initMode = "degree", weightCol = "weight") |
| ##D showDF(clusters) |
| ## End(Not run) |
| </code></pre> |
| |
| |
| <hr /><div style="text-align: center;">[Package <em>SparkR</em> version 3.2.2 <a href="00Index.html">Index</a>]</div> |
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