Data Discretization with K-Means ClusteringΒΆ

Non-Causal Step

K-Means Clustering algorithm is first used to partition the input data values into clusters. Then, the discretization strategy for the input data is made using the information of maximum and minimum values of the data set, computed cluster centers and midpoints between each two clusters.

Input Parameters

  1. Input data

Output Parameters

  1. Discretized data

Workflow

../../../_images/workflow45.svg

Algorithm

K-Means Clustering

References

  • J. Han, M. Kamber and J. Pei, Data Mining - Concepts and Techniques, 3rd ed., Amsterdam: Morgan Kaufmann Publishers, 2012.