상세정보
학술지
Network-Based Document Clustering Using External Ranking Loss for Network Embedding
- 발행일
- 201910
- 출처
- IEEE Access, v.7, pp.155412-155423
- ISSN
- 2169-3536
- 출판사
- IEEE
- 협약과제
- 19HS4700, 디지털콘텐츠 In-House R&D, 박수명
- 초록
- 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 제안 키워드
- Clustering method, Common word, Data analysis, Data representation, K-means algorithm, Learning methods, Network Embedding, Network Graph, Probabilistic generative model, Ranking-based, calculation method