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Detail
Journal Article
Network-Based Document Clustering Using External Ranking Loss for Network Embedding
- Authors
- Yeo Chan Yoon, Hyung Kuen Gee, Heuiseok Lim
- Issue Date
- 2019-10
- Citation
- IEEE Access, v.7, pp.155412-155423
- ISSN
- 2169-3536
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- 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.
- KSP Keywords
- Clustering method, Common word, Data analysis, Data representation, Embedding method, Fine grained(FG), Learning methods, Network embedding, Network-based, Probabilistic generative model, Ranking-based
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
