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 <table width="100%" summary="page for spark.bisectingKmeans {SparkR}"><tr><td>spark.bisectingKmeans {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table>

 <h2>Bisecting K-Means Clustering Model</h2>

 <h3>Description</h3>

 <p>Fits a bisecting k-means clustering model against a SparkDataFrame.
 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>
 <p>Get fitted result from a bisecting k-means model.
 Note: A saved-loaded model does not support this method.
 </p>


 <h3>Usage</h3>

 <pre>
 spark.bisectingKmeans(data, formula, ...)

 ## S4 method for signature 'SparkDataFrame,formula'
 spark.bisectingKmeans(data, formula,
   k = 4, maxIter = 20, seed = NULL, minDivisibleClusterSize = 1)

 ## S4 method for signature 'BisectingKMeansModel'
 summary(object)

 ## S4 method for signature 'BisectingKMeansModel'
 predict(object, newData)

 ## S4 method for signature 'BisectingKMeansModel'
 fitted(object, method = c("centers",
   "classes"))

 ## S4 method for signature 'BisectingKMeansModel,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.bisectingKmeans.</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>the desired number of leaf clusters. Must be &gt; 1.
 The actual number could be smaller if there are no divisible leaf clusters.</p>
 </td></tr>
 <tr valign="top"><td><code>maxIter</code></td>
 <td>
 <p>maximum iteration number.</p>
 </td></tr>
 <tr valign="top"><td><code>seed</code></td>
 <td>
 <p>the random seed.</p>
 </td></tr>
 <tr valign="top"><td><code>minDivisibleClusterSize</code></td>
 <td>
 <p>The minimum number of points (if greater than or equal to 1.0)
 or the minimum proportion of points (if less than 1.0) of a
 divisible cluster. Note that it is an expert parameter. The
 default value should be good enough for most cases.</p>
 </td></tr>
 <tr valign="top"><td><code>object</code></td>
 <td>
 <p>a fitted bisecting 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>method</code></td>
 <td>
 <p>type of fitted results, <code>"centers"</code> for cluster centers
 or <code>"classes"</code> for assigned classes.</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.bisectingKmeans</code> returns a fitted bisecting 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> (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; cluster is NULL if is.loaded is TRUE),
 and <code>is.loaded</code> (whether the model is loaded from a saved file).
 </p>
 <p><code>predict</code> returns the predicted values based on a bisecting k-means model.
 </p>
 <p><code>fitted</code> returns a SparkDataFrame containing fitted values.
 </p>


 <h3>Note</h3>

 <p>spark.bisectingKmeans since 2.2.0
 </p>
 <p>summary(BisectingKMeansModel) since 2.2.0
 </p>
 <p>predict(BisectingKMeansModel) since 2.2.0
 </p>
 <p>fitted since 2.2.0
 </p>
 <p>write.ml(BisectingKMeansModel, character) since 2.2.0
 </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 t &lt;- as.data.frame(Titanic)
 ##D df &lt;- createDataFrame(t)
 ##D model &lt;- spark.bisectingKmeans(df, Class ~ Survived, k = 4)
 ##D summary(model)
 ##D
 ##D # get fitted result from a bisecting k-means model
 ##D fitted.model &lt;- fitted(model, &quot;centers&quot;)
 ##D showDF(fitted.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 2.3.2 <a href="00Index.html">Index</a>]</div>
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	<table width="100%" summary="page for spark.bisectingKmeans {SparkR}"><tr><td>spark.bisectingKmeans {SparkR}</td><td style="text-align: right;">R Documentation</td></tr></table>

	<h2>Bisecting K-Means Clustering Model</h2>

	<h3>Description</h3>

	<p>Fits a bisecting k-means clustering model against a SparkDataFrame.
	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>
	<p>Get fitted result from a bisecting k-means model.
	Note: A saved-loaded model does not support this method.
	</p>


	<h3>Usage</h3>

	<pre>
	spark.bisectingKmeans(data, formula, ...)

	## S4 method for signature 'SparkDataFrame,formula'
	spark.bisectingKmeans(data, formula,
	k = 4, maxIter = 20, seed = NULL, minDivisibleClusterSize = 1)

	## S4 method for signature 'BisectingKMeansModel'
	summary(object)

	## S4 method for signature 'BisectingKMeansModel'
	predict(object, newData)

	## S4 method for signature 'BisectingKMeansModel'
	fitted(object, method = c("centers",
	"classes"))

	## S4 method for signature 'BisectingKMeansModel,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.bisectingKmeans.</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>the desired number of leaf clusters. Must be > 1.
	The actual number could be smaller if there are no divisible leaf clusters.</p>
	</td></tr>
	<tr valign="top"><td><code>maxIter</code></td>
	<td>
	<p>maximum iteration number.</p>
	</td></tr>
	<tr valign="top"><td><code>seed</code></td>
	<td>
	<p>the random seed.</p>
	</td></tr>
	<tr valign="top"><td><code>minDivisibleClusterSize</code></td>
	<td>
	<p>The minimum number of points (if greater than or equal to 1.0)
	or the minimum proportion of points (if less than 1.0) of a
	divisible cluster. Note that it is an expert parameter. The
	default value should be good enough for most cases.</p>
	</td></tr>
	<tr valign="top"><td><code>object</code></td>
	<td>
	<p>a fitted bisecting 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>method</code></td>
	<td>
	<p>type of fitted results, <code>"centers"</code> for cluster centers
	or <code>"classes"</code> for assigned classes.</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.bisectingKmeans</code> returns a fitted bisecting 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> (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; cluster is NULL if is.loaded is TRUE),
	and <code>is.loaded</code> (whether the model is loaded from a saved file).
	</p>
	<p><code>predict</code> returns the predicted values based on a bisecting k-means model.
	</p>
	<p><code>fitted</code> returns a SparkDataFrame containing fitted values.
	</p>


	<h3>Note</h3>

	<p>spark.bisectingKmeans since 2.2.0
	</p>
	<p>summary(BisectingKMeansModel) since 2.2.0
	</p>
	<p>predict(BisectingKMeansModel) since 2.2.0
	</p>
	<p>fitted since 2.2.0
	</p>
	<p>write.ml(BisectingKMeansModel, character) since 2.2.0
	</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 t <- as.data.frame(Titanic)
	##D df <- createDataFrame(t)
	##D model <- spark.bisectingKmeans(df, Class ~ Survived, k = 4)
	##D summary(model)
	##D
	##D # get fitted result from a bisecting k-means model
	##D fitted.model <- fitted(model, "centers")
	##D showDF(fitted.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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