DOI QR코드

DOI QR Code

합성곱 오토인코더 기반의 응집형 계층적 군집 분석

Agglomerative Hierarchical Clustering Analysis with Deep Convolutional Autoencoders

  • Park, Nojin (School of Electrical Engineering, Korea University) ;
  • Ko, Hanseok (School of Electrical Engineering, Korea University)
  • 투고 : 2019.11.12
  • 심사 : 2019.12.16
  • 발행 : 2020.01.31

초록

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.

과제정보

연구 과제 주관 기관 : Korea Health Industry Development Institute (KHIDI)

참고문헌

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