DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning
This addresses clustering accuracy issues in applications like market research and image processing, but it is incremental as it builds on existing deep learning and clustering methods.
The paper tackles the problem of inaccurate clustering due to high-dimensional noisy data by proposing DAC, a deep autoencoder-based framework for representation learning, which effectively boosts K-Means clustering performance on various datasets.
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.