# tKMeansStrModel properties for Apache Spark Streaming These properties are used to configure tKMeansStrModel running in the Spark Streaming Job framework. The Spark Streaming tKMeansStrModel component belongs to the Machine Learning family. This component is available in Talend Real Time Big Data Platform and Talend Data Fabric. ## Basic settings | Save on disk | Select this check box to store the clustering model in an HDFS
directory you put in the Path
field.

In this case, you need to enter the time interval (in minutes) at the
end of which the model is saved.

If you clear this check box, your model will be stored in
memory. | | --- | --- | | Path | Select this check box to store the model in a given file system. Otherwise, the model is
stored in memory. The button for browsing does not work with the Spark Local mode; if you are using the Spark Yarn
or the Spark Standalone mode, ensure that you have properly
configured the connection in a configuration component in the same Job, such as tHDFSConfiguration.

In the Path field, enter the HDFS
directory to be used.

This field is available when you select the check boxes used to save a model to or read a model from a file system. | | Load a precomputed model from
disk | Select this check box to use an existing K-Means model stored in the
directory you have specified in the Path field. This is the common case when using tKMeansStrModel. In this situation, the following
behaviors can be expected:



If you clear this Load a precomputed model from
disk check box, tKMeansStrModel will create a new K-Means model from
scratch. | | Vector to process | Select the input column used to provide feature vectors. Very often,
this column is the output of the feature engineering computations
performed by tModelEncoder.

This list appears when you have cleared either the Load a precomputed model from disk check box
or the Reuse the model transformation associated
with the model check box. | | Size of your feature vector | Enter the size of the feature vectors to be processed from the column
you have selected from the Vector to
process list. | | Display the vector size | Select this check box to display the feature vectors to be used in the
console of the Run view.

This feature will slow down your Job but is useful when you do not
know what value to be entered in the Size of your
feature vector field. | | Number of clusters (K) | Enter the number of clusters into which you want tKMeansModel to cluster data.

In general, a large number of clusters can decreases errors in
predictions but increases the risk of overfitting.

This field appears when you have cleared the Load a precomputed model from disk check box to create a
K-Means model from scratch. | | Decay factor | Enter the decay rate (ranging between 0 and 1) to be applied to
discount the weight of existing points against the new incoming points
in the process of evaluating new cluster centers.

Lower decay rate means more importance to be attached to the new
incoming data. When decay rate is *0*,
new cluster centers are determined completely by the new points; when
decay rate is *1*, the existing points
and new incoming points are evaluated equally. | | Time unit | Select the unit on which the decay rate is applied: point or batch of
points. | ## Advanced settings | Display the centers after the
processing | Select this check box to output the vectors of the cluster centers
into the console of the Run
view.

This feature is often useful when you need to understand how the
cluster centers move in the process of training your K-Means
model. | | --- | --- | ## Usage | **Usage rule** | This component is used as an end component and requires an input link. | | --- | --- | | Model evaluation | The parameters you need to set are free parameters and so their values may be provided by
previous experiments, empirical guesses or the like. They do not have any optimal values
applicable for all datasets.

Therefore, you need to train the relationship model you are generating with different sets
of parameter values until you can obtain the best evaluation result. But note that you need
to write the evaluation code yourself to rank your model with scores. |