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| <table width="100%" summary="page for spark.kmeans {SparkR}"><tr><td>spark.kmeans {SparkR}</td><td align="right">R Documentation</td></tr></table> |
| |
| <h2>K-Means Clustering Model</h2> |
| |
| <h3>Description</h3> |
| |
| <p>Fits a k-means clustering model against a SparkDataFrame, similarly to R's kmeans(). |
| Users can call <code>summary</code> to print a summary of the fitted model, <code>predict</code> to make |
| predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to save/load fitted models. |
| </p> |
| |
| |
| <h3>Usage</h3> |
| |
| <pre> |
| spark.kmeans(data, formula, ...) |
| |
| ## S4 method for signature 'SparkDataFrame,formula' |
| spark.kmeans(data, formula, k = 2, |
| maxIter = 20, initMode = c("k-means||", "random"), seed = NULL, |
| initSteps = 2, tol = 1e-04) |
| |
| ## S4 method for signature 'KMeansModel' |
| summary(object) |
| |
| ## S4 method for signature 'KMeansModel' |
| predict(object, newData) |
| |
| ## S4 method for signature 'KMeansModel,character' |
| write.ml(object, path, overwrite = FALSE) |
| </pre> |
| |
| |
| <h3>Arguments</h3> |
| |
| <table summary="R argblock"> |
| <tr valign="top"><td><code>data</code></td> |
| <td> |
| <p>a SparkDataFrame for training.</p> |
| </td></tr> |
| <tr valign="top"><td><code>formula</code></td> |
| <td> |
| <p>a symbolic description of the model to be fitted. Currently only a few formula |
| operators are supported, including '~', '.', ':', '+', and '-'. |
| Note that the response variable of formula is empty in spark.kmeans.</p> |
| </td></tr> |
| <tr valign="top"><td><code>...</code></td> |
| <td> |
| <p>additional argument(s) passed to the method.</p> |
| </td></tr> |
| <tr valign="top"><td><code>k</code></td> |
| <td> |
| <p>number of centers.</p> |
| </td></tr> |
| <tr valign="top"><td><code>maxIter</code></td> |
| <td> |
| <p>maximum iteration number.</p> |
| </td></tr> |
| <tr valign="top"><td><code>initMode</code></td> |
| <td> |
| <p>the initialization algorithm choosen to fit the model.</p> |
| </td></tr> |
| <tr valign="top"><td><code>seed</code></td> |
| <td> |
| <p>the random seed for cluster initialization</p> |
| </td></tr> |
| <tr valign="top"><td><code>initSteps</code></td> |
| <td> |
| <p>the number of steps for the k-means|| initialization mode. |
| This is an advanced setting, the default of 2 is almost always enough. Must be > 0.</p> |
| </td></tr> |
| <tr valign="top"><td><code>tol</code></td> |
| <td> |
| <p>convergence tolerance of iterations.</p> |
| </td></tr> |
| <tr valign="top"><td><code>object</code></td> |
| <td> |
| <p>a fitted k-means model.</p> |
| </td></tr> |
| <tr valign="top"><td><code>newData</code></td> |
| <td> |
| <p>a SparkDataFrame for testing.</p> |
| </td></tr> |
| <tr valign="top"><td><code>path</code></td> |
| <td> |
| <p>the directory where the model is saved.</p> |
| </td></tr> |
| <tr valign="top"><td><code>overwrite</code></td> |
| <td> |
| <p>overwrites or not if the output path already exists. Default is FALSE |
| which means throw exception if the output path exists.</p> |
| </td></tr> |
| </table> |
| |
| |
| <h3>Value</h3> |
| |
| <p><code>spark.kmeans</code> returns a fitted k-means model. |
| </p> |
| <p><code>summary</code> returns summary information of the fitted model, which is a list. |
| The list includes the model's <code>k</code> (the configured number of cluster centers), |
| <code>coefficients</code> (model cluster centers), |
| <code>size</code> (number of data points in each cluster), <code>cluster</code> |
| (cluster centers of the transformed data), is.loaded (whether the model is loaded |
| from a saved file), and <code>clusterSize</code> |
| (the actual number of cluster centers. When using initMode = "random", |
| <code>clusterSize</code> may not equal to <code>k</code>). |
| </p> |
| <p><code>predict</code> returns the predicted values based on a k-means model. |
| </p> |
| |
| |
| <h3>Note</h3> |
| |
| <p>spark.kmeans since 2.0.0 |
| </p> |
| <p>summary(KMeansModel) since 2.0.0 |
| </p> |
| <p>predict(KMeansModel) since 2.0.0 |
| </p> |
| <p>write.ml(KMeansModel, character) since 2.0.0 |
| </p> |
| |
| |
| <h3>See Also</h3> |
| |
| <p><a href="predict.html">predict</a>, <a href="read.ml.html">read.ml</a>, <a href="write.ml.html">write.ml</a> |
| </p> |
| |
| |
| <h3>Examples</h3> |
| |
| <pre><code class="r">## Not run: |
| ##D sparkR.session() |
| ##D data(iris) |
| ##D df <- createDataFrame(iris) |
| ##D model <- spark.kmeans(df, Sepal_Length ~ Sepal_Width, k = 4, initMode = "random") |
| ##D summary(model) |
| ##D |
| ##D # fitted values on training data |
| ##D fitted <- predict(model, df) |
| ##D head(select(fitted, "Sepal_Length", "prediction")) |
| ##D |
| ##D # save fitted model to input path |
| ##D path <- "path/to/model" |
| ##D write.ml(model, path) |
| ##D |
| ##D # can also read back the saved model and print |
| ##D savedModel <- read.ml(path) |
| ##D summary(savedModel) |
| ## End(Not run) |
| </code></pre> |
| |
| |
| <hr><div align="center">[Package <em>SparkR</em> version 2.1.1 <a href="00Index.html">Index</a>]</div> |
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