Network-based document clustering using external ranking loss for network embedding

Yeo Chan Yoon, Hyung Kuen Gee, Heuiseok Lim

Research output: Contribution to journalArticlepeer-review

Abstract

Network-based document clustering involves forming clusters of documents based on their significance and relationship strength. This approach can be used with various types of metadata that express the significance of the documents and the relationships among them. In this study, we defined a probabilistic network graph for fine-grained document clustering and developed a probabilistic generative model and calculation method. Furthermore, a novel neural-network-based network embedding learning method was devised that considers the significance of a document based on its rankings with external measures, such as the download counts of relevant files, and reflects the relationship strength between the documents. By considering the significance of a document, reputative documents of clusters can be centralized and shown as representative documents for tasks such as data analysis and data representation. During evaluation tests, the proposed ranking-based network-embedding method performs significantly better on various algorithms, such as the k-means algorithm and common word/phrase-based clustering methods, than the existing network embedding approaches.

Original languageEnglish
Article number8878093
Pages (from-to)155412-155423
Number of pages12
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

Keywords

  • Clustering algorithms
  • artificial neural networks

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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