Machine Learning
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
KeyGraph: Automatic Indexing by Co-occurrence Graph based on Building Construction Metaphor
ADL '98 Proceedings of the Advances in Digital Libraries Conference
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In this research, we propose an integrated subject graph which expresses the subject of the document. The proposed integrated subject graph is based on the graph-based text representation model which is called "subject graph". In the subject graph, a node represents a term in the text, and an edge denotes a relation between linked terms. As the conventional text representation models, the graph models such as the subject graph and the KeyGraph have been proposed, and most of them assume that one document has one subject. However, the document often has not only one subject but also plural subjects. In this research, we assume that each unit of the document such as a paragraph has one subject, and each unit is translated into a subject graph. Then, they are integrated into an integrated subject graph. In this research, we apply the proposed integrated subject graph to the document clustering and realize the document clustering based on the similarity of the subjects. We carried out a series of computer experiments and confirmed the effectiveness of the proposed integrated subject graph.