August 15, 2023

Preparing features for KMeans – Docs for ESB 6.x

  2. Talend for ESB Docs
  3. Docs 6.x
  4. Preparing features for KMeans – Docs for ESB 6.x

Preparing features for KMeans

  1. Double-click the tModelEncoder component to open its Component view.

    use_case-kmeans_feature_vector.png

  2. Click the […] button
    next to Edit schema and on the tModelEncoder side of the pop-up schema dialog box,
    define the schema by adding one column named map of
    Vector type.
  3. Click OK to validate these changes and accept
    the propagation prompted by the pop-up dialog box.
  4. In the Transformations
    table, add one row by clicking the [+]
    button and then proceed as follows:

    1. In the Output column column, select the column
      that carry the features. In this scenario, it is
      map.
    2. In the Transformation column, select the
      algorithm to be used for the transformation. In this scenario, it is
      Vector assembler.
    3. In the Parameters column, enter the parameters
      you want to customize for use in the Vector assembler algorithm. In this
      scenario, enter
      inputCols=latitude,longitude.
    In this transformation, tModelEncoder combines all feature vectors into one single
    feature column.
  5. Double-click tKMeansModel to open its Component view.

    use_case-kmeans_model_training.png

  6. Select the Define a storage configuration
    component     check box and select the tHDFSConfiguration component to be used.
  7. From the Vector to
    process     list, select the column that provides the feature vectors to
    be analyzed. In this scenario, it is map, which combines
    all features.
  8. Select the Save the model on file system
    check box and in the HDFS folder field that
    is displayed, enter the directory you want to use to store the generated
    model.
  9. In the Number of cluster
    field, enter the number of decision trees you want tKMeans to build. You need to try different numbers to run the
    current Job to create the clustering model several times; after comparing the
    evaluation results of every model created on each run, you can decide the number
    you need to use. For example, put 6.

    You need to write the evaluation code yourself.
  10. From the Initialization function, select Random. In general, this mode is used for simple datasets.
  11. Leave the other parameters as they are.
Document get from Talend https://help.talend.com
Thank you for watching.
Docs 6.x
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