  Clustering performs an essential role in many real world applications, such
as market research, pattern recognition, data analysis, and image processing.
However, due to the high dimensionality of the input feature values, the data
being fed to clustering algorithms usually contains noise and thus could lead
to in-accurate clustering results. While traditional dimension reduction and
feature selection algorithms could be used to address this problem, the simple
heuristic rules used in those algorithms are based on some particular
assumptions. When those assumptions does not hold, these algorithms then might
not work. In this paper, we propose DAC, Deep Autoencoder-based Clustering, a
generalized data-driven framework to learn clustering representations using
deep neuron networks. Experiment results show that our approach could
effectively boost performance of the K-Means clustering algorithm on a variety
types of datasets.
