Clustering performs an essential role in many real world applications, such as market research, pattern recognition, data analysis, and image processing . Due to the high dimensionality of the input feature values, the databeing fed to clustering algorithms usually contains noise and thus could lead to in-accurate clustering results . In this paper, we propose DAC, Deep Autoencoder-based Clustered, ageneralized data-driven framework to learn clustering representations using deep neuron networks . Experiment results show that our approach could boost performance of the K-Means clustering algorithm on a variety of datasets .

Author(s) : Si Lu, Ruisi Li

Links : PDF - Abstract

Code :

https://github.com/oktantod/RoboND-DeepLearning-Project


Coursera

Keywords : clustering - deep - learning - dac - autoencoder -

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