Agglomerative Hierarchical Clustering Analysis with Deep Convolutional Autoencoders

합성곱 오토인코더 기반의 응집형 계층적 군집 분석

  • Park, Nojin (School of Electrical Engineering, Korea University) ;
  • Ko, Hanseok (School of Electrical Engineering, Korea University)
  • Received : 2019.11.12
  • Accepted : 2019.12.16
  • Published : 2020.01.31


Clustering methods essentially take a two-step approach; extracting feature vectors for dimensionality reduction and then employing clustering algorithm on the extracted feature vectors. However, for clustering images, the traditional clustering methods such as stacked auto-encoder based k-means are not effective since they tend to ignore the local information. In this paper, we propose a method first to effectively reduce data dimensionality using convolutional auto-encoder to capture and reflect the local information and then to accurately cluster similar data samples by using a hierarchical clustering approach. The experimental results confirm that the clustering results are improved by using the proposed model in terms of clustering accuracy and normalized mutual information.


Supported by : Korea Health Industry Development Institute (KHIDI)


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