The clustering models we covered in the previous chapter can also be used for a form of dimensionality reduction. This works in the following way:

- Assume that we cluster our high-dimensional feature vectors using a K-means clustering model, with k clusters. The result is a set of
*k*cluster centers.

- We can represent each of our original data points in terms of how far it is from each of these cluster centers. That is, we can compute the distance of a data point to each cluster center. The result is a set of
*k*distances for each data point.

- These
*k*distances can form a new vector of dimension*k*. We can now represent our original data as a new vector of lower dimension relative to the original feature ...