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학술지 Network-Based Document Clustering Using External Ranking Loss for Network Embedding
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저자
윤여찬, 지형근, 임희석
발행일
201910
출처
IEEE Access, v.7, pp.155412-155423
ISSN
2169-3536
출판사
IEEE
DOI
https://dx.doi.org/10.1109/ACCESS.2019.2948662
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.
키워드
artificial neural networks, Clustering algorithms
KSP 제안 키워드
Artificial neural networks, Clustering algorithm, Clustering method, Common word, Data analysis, Data representation, K-means algorithm, Learning methods, Network Embedding, Network Graph, Probabilistic generative model