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Data Reduction with K-Means Clustering
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:doc:`/SingleStepClassifiers/NonCausalStep/index`

This step applies the *K-Means Clustering* algorithm to reduce the size of input data set without losing much information.

.. rubric:: Input Parameters

1. Large size of input data

2. Specified number of clusters

.. rubric:: Output Parameters

1. Reduced data set

.. rubric:: Workflow

.. image:: workflow.svg


.. rubric:: Algorithm

:doc:`/Algorithms/KMeansClustering/index`

.. rubric:: References

- Z.F.\  Hao, W. Wen, X.W. Yang, J. Lu, and G.Q. Zhang, A Fast Data Preprocessing Procedure for Support Vector Regression, IDEAL 2006, LNCS 4224, pp. 48-56, 2006.