Using StreamingML Kmeans for Clustering¶
Purpose¶
This sample demonstrates how to use siddhi-execution-streamingml kmeans incremental function for clustering.
Prerequisites¶
Save this sample. If there is no syntax error, the following messages would be shown on the console.
* Siddhi App StreamingKMeansSample successfully deployed.Executing the Sample¶
- Start the Siddhi application by clicking on 'Run'.
- If the Siddhi application starts successfully, the following messages would be shown on the console.
* StreamingKMeansSample.siddhi - Started Successfully!
Testing the Sample¶
You can publish data event to the file, through event simulator. 1. Open event simulator by clicking on the second icon or press Ctrl+Shift+I. 2. In the Single Simulation tab of the panel, select values as follows: * Siddhi App Name : StreamingKMeansSample * Stream Name : SweetProductionStream 3. Enter and send suitable values for the attributes of selected stream.
Viewing the Results¶
Messages similar to the following would be shown on the console.
INFO {io.siddhi.core.stream.output.sink.LogSink} - StreamingMLExtensionkmeans-incremental-sample : SweetStatePredictionStream : Event{timestamp=1513603080892, data=[12.5, 124.5, 12.5, 124.5], isExpired=false}First two values of the data array represent the coordinates of the cluster that given product belongs to.
(eg: 12.5, 124.5)@App:name("StreamingKMeansSample")
@App:Description('Demonstrates how to use siddhi-execution-streamingml kmeans incremental function for clustering.')
define stream SweetProductionStream(temperature double, density double);
@sink(type='log')
define stream SweetStatePredictionStream(closestCentroidCoordinate1 double, closestCentroidCoordinate2 double, temperature double, density double);
@info(name = 'query1')
from SweetProductionStream#streamingml:kMeansIncremental(2, 0.2, temperature, density)
select closestCentroidCoordinate1, closestCentroidCoordinate2, temperature, density
insert into SweetStatePredictionStream;