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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:

  1. Start the Siddhi application by clicking on 'Run'.
  2. 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} - SteamingMLExtensionkmeans-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;
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