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Is Clustering Dimensionality Reduction?

Sophia Al-Mansoor • • 6 min read
Is Clustering Dimensionality Reduction?

Dimensionality Reduction: Assign m features to p components with some weight or probability. Soft Clustering: Assign observations to k clusters

k clusters
Three key features of k-means that make it efficient are often regarded as its biggest drawbacks: Euclidean distance is used as a metric and variance is used as a measure of cluster scatter. The number of clusters k is an input parameter: an inappropriate choice of k may yield poor results.
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with some weight or probability. Dimensionality Reduction: Assign m features to p components with some weight or probability.

Is clustering a dimensionality reduction technique?

You can also consider K-means as a dimension reduction technique. If you have an N dimension data and you cluster them into C clusters, then you basically reduced the dimension from N to C. If you are using a a hard K-means clustering then for sure the output will be binary and not useful for dimension reduction.

Is cluster analysis a dimension reduction?

Dimension reduction is important in cluster analysis and creates a smaller data in volume and has the same analytical results as the original representation. A clustering process needs data reduction to obtain an efficient processing time while clustering and mitigate curse of dimensionality.

Sophia Al-Mansoor
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Sophia Al-Mansoor

Sophia analyzes international trade, startup ecosystems, retail transformation, and supply chain logistics for modern digital publications.