ML Anthology

DeepCluster: A General Clustering Framework Based on Deep Learning

Kai Tian, Shuigeng Zhou, Jihong Guan
ECML-PKDD 2017 pp. 809-825
doi:10.1007/978-3-319-71246-8_49 /ecmlpkdd/2017/tian2017ecmlpkdd-deepcluster/

Abstract

In this paper, we propose a general framework DeepCluster to integrate traditional clustering methods into deep learning (DL) models and adopt Alternating Direction of Multiplier Method (ADMM) to optimize it. While most existing DL based clustering techniques have separate feature learning (via DL) and clustering (with traditional clustering methods), DeepCluster simultaneously learns feature representation and does cluster assignment under the same framework. Furthermore, it is a general and flexible framework that can employ different networks and clustering methods. We demonstrate the effectiveness of DeepCluster by integrating two popular clustering methods: K-means and Gaussian Mixture Model (GMM) into deep networks. The experimental results shown that our method can achieve state-of-the-art performance on learning representation for clustering analysis. Code and data related to this chapter are available at: https://github.com/JennyQQL/DeepClusterADMM-Release .

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Cite

Text

Tian et al. "DeepCluster: A General Clustering Framework Based on Deep Learning." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017. doi:10.1007/978-3-319-71246-8_49

Markdown

[Tian et al. "DeepCluster: A General Clustering Framework Based on Deep Learning." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2017.](https://mlanthology.org/ecmlpkdd/2017/tian2017ecmlpkdd-deepcluster/) doi:10.1007/978-3-319-71246-8_49

BibTeX

@inproceedings{tian2017ecmlpkdd-deepcluster,
  title     = {{DeepCluster: A General Clustering Framework Based on Deep Learning}},
  author    = {Tian, Kai and Zhou, Shuigeng and Guan, Jihong},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2017},
  pages     = {809-825},
  doi       = {10.1007/978-3-319-71246-8_49},
  url       = {https://mlanthology.org/ecmlpkdd/2017/tian2017ecmlpkdd-deepcluster/}
}