DAC: Deep Autoencoder-based Clustering, a General Deep Learning Framework of Representation Learning

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 c...

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Bibliographic Details
Published inarXiv.org
Main Authors Lu, Si, Li, Ruisi
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 15.02.2021
Subjects
Algorithms
Cluster analysis
Clustering
Data analysis
Deep learning
Image processing
Object recognition
Pattern analysis
Pattern recognition
Representations
Vector quantization
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Summary: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.
ISSN:2331-8422