Deep Continuous Clustering

Understand

Clustering high-dimensional datasets is hard because interpoint distances become less informative in high-dimensional spaces.

• We present a clustering algorithm that performs nonlinear dimensionality reduction and clustering jointly.
• The data is embedded into a lower-dimensional space by a deep autoencoder.
• The autoencoder is optimized as part of the clustering process.