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 <table style="width: 100%;"><tr><td>spark.kmeans {SparkR}</td><td style="text-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><code class='language-R'>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)
 </code></pre>


 <h3>Arguments</h3>

 <table>
 <tr style="vertical-align: top;"><td><code>data</code></td>
 <td>
 <p>a SparkDataFrame for training.</p>
 </td></tr>
 <tr style="vertical-align: 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 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>number of centers.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>maxIter</code></td>
 <td>
 <p>maximum iteration number.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>initMode</code></td>
 <td>
 <p>the initialization algorithm chosen to fit the model.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>seed</code></td>
 <td>
 <p>the random seed for cluster initialization.</p>
 </td></tr>
 <tr style="vertical-align: 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 &gt; 0.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>tol</code></td>
 <td>
 <p>convergence tolerance of iterations.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>object</code></td>
 <td>
 <p>a fitted k-means model.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>newData</code></td>
 <td>
 <p>a SparkDataFrame for testing.</p>
 </td></tr>
 <tr style="vertical-align: top;"><td><code>path</code></td>
 <td>
 <p>the directory where the model is saved.</p>
 </td></tr>
 <tr style="vertical-align: 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 = &quot;random&quot;,
 <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="../../SparkR/help/predict.html">predict</a>, <a href="../../SparkR/help/read.ml.html">read.ml</a>, <a href="../../SparkR/help/write.ml.html">write.ml</a>
 </p>


 <h3>Examples</h3>

 <pre><code class="r">## Not run:
 ##D sparkR.session()
 ##D t &lt;- as.data.frame(Titanic)
 ##D df &lt;- createDataFrame(t)
 ##D model &lt;- spark.kmeans(df, Class ~ Survived, k = 4, initMode = &quot;random&quot;)
 ##D summary(model)
 ##D
 ##D # fitted values on training data
 ##D fitted &lt;- predict(model, df)
 ##D head(select(fitted, &quot;Class&quot;, &quot;prediction&quot;))
 ##D
 ##D # save fitted model to input path
 ##D path &lt;- &quot;path/to/model&quot;
 ##D write.ml(model, path)
 ##D
 ##D # can also read back the saved model and print
 ##D savedModel &lt;- read.ml(path)
 ##D summary(savedModel)
 ## 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>
 </div>
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	<table style="width: 100%;"><tr><td>spark.kmeans {SparkR}</td><td style="text-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><code class='language-R'>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)
	</code></pre>


	<h3>Arguments</h3>

	<table>
	<tr style="vertical-align: top;"><td><code>data</code></td>
	<td>
	<p>a SparkDataFrame for training.</p>
	</td></tr>
	<tr style="vertical-align: 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 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>number of centers.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>maxIter</code></td>
	<td>
	<p>maximum iteration number.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>initMode</code></td>
	<td>
	<p>the initialization algorithm chosen to fit the model.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>seed</code></td>
	<td>
	<p>the random seed for cluster initialization.</p>
	</td></tr>
	<tr style="vertical-align: 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 style="vertical-align: top;"><td><code>tol</code></td>
	<td>
	<p>convergence tolerance of iterations.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>object</code></td>
	<td>
	<p>a fitted k-means model.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>newData</code></td>
	<td>
	<p>a SparkDataFrame for testing.</p>
	</td></tr>
	<tr style="vertical-align: top;"><td><code>path</code></td>
	<td>
	<p>the directory where the model is saved.</p>
	</td></tr>
	<tr style="vertical-align: 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="../../SparkR/help/predict.html">predict</a>, <a href="../../SparkR/help/read.ml.html">read.ml</a>, <a href="../../SparkR/help/write.ml.html">write.ml</a>
	</p>


	<h3>Examples</h3>

	<pre><code class="r">## Not run:
	##D sparkR.session()
	##D t <- as.data.frame(Titanic)
	##D df <- createDataFrame(t)
	##D model <- spark.kmeans(df, Class ~ Survived, k = 4, initMode = "random")
	##D summary(model)
	##D
	##D # fitted values on training data
	##D fitted <- predict(model, df)
	##D head(select(fitted, "Class", "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>


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