How do autoencoders help in data mining tasks?

Autoencoders learn compressed representations of data for reconstruction and feature extraction.

Autoencoders are neural networks trained to reconstruct input data through a bottleneck latent layer. This forces the model to learn compact representations capturing essential structure. In data mining, they are used for dimensionality reduction, anomaly detection, and feature learning, especially for high-dimensional or unstructured data.