  Training of a Machine Learning model requires sufficient data. The
sufficiency of the data is not always about the quantity, but about the
relevancy and reduced redundancy. Data-generating processes create massive
amounts of data. When used raw, such big data is causing much computational
resource utilization. Instead of using the raw data, a proper Condensed
Representation can be used instead. Combining K-means, a well-known clustering
method, with some correction and refinement facilities a novel Condensed
Representation method for Machine Learning applications is introduced. To
present the novel method meaningfully and visually, synthetically generated
data is employed. It has been shown that by using the condensed representation,
instead of the raw data, acceptably accurate model training is possible.
