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;