AutoClustering: A feed-forward neural network based clustering algorithm

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Since a clustering process can be regarded as a map of data to cluster labels, it should be natural to employ a deep learning technique, especially a feed-forward neural network, to realize the clustering method. In this study, we discussed a novel clustering method realized only by a feed-forward neural network. Unlike self-organizing maps and growing neural gas networks, the proposed method is compatible with deep learning neural networks. The proposed method has three parts: A map of records to clusters (encoder), a map of clusters to their exemplars (decoder), and a loss function to measure positional closeness between the records and the exemplars. In order to accelerate clustering performance, we proposed an improved activation function at the encoder, which migrates a soft-max function to a max function continuously. Though most of the clustering methods require the number of clusters in advance, the proposed method naturally provides the number of clusters as the number of unique one-hot vectors obtained as a result. We also discussed the existence of local minima of the loss function and their relationship to clusters.

本文言語English
ホスト出版物のタイトルProceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
編集者Zhenhui Li, Jeffrey Yu, Hanghang Tong, Feida Zhu
出版社IEEE Computer Society
ページ659-666
ページ数8
ISBN(電子版)9781538692882
DOI
出版ステータスPublished - 2019 2 7
イベント18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 - Singapore, Singapore
継続期間: 2018 11 172018 11 20

出版物シリーズ

名前IEEE International Conference on Data Mining Workshops, ICDMW
2018-November
ISSN(印刷版)2375-9232
ISSN(電子版)2375-9259

Conference

Conference18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
CountrySingapore
CitySingapore
Period18/11/1718/11/20

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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