The Community for Technology Leaders
Computer and Information Technology, International Conference on (2010)
Bradford, West Yorkshire, UK
June 29, 2010 to July 1, 2010
ISBN: 978-0-7695-4108-2
pp: 1280-1284
With the tremendous growth in electronic publication, locating the most relevant references is becoming a challenging task. Most effective document indexing structures represent a document as a vector of very high dimensionality. It is well known that such a representation suffers from the curse of dimensionality. In this paper, we introduce DT-Tree (DocumentTerm-Tree) - a new structure for the representation of scientific documents. DT-Tree represents a document using the 50 most frequent terms in that specific document. These terms are grouped into a tree structure according to where they appear in the document, such as title, abstract, or section title, etc. The distance between two documents is calculated based on their DT-Trees. Two DTTrees are compared using Dice coefficient between the corresponding nodes of the trees. To verify the effectiveness of our similarity measure, we conducted experiments to cluster 150 documents in three categories, namely biology [1], chemistry [2-3] and physics [3]. The experimental results indicated 100% accuracy.
Analysis, Similarity Measure, Document clustering, dimension reduction, key term extraction, sparsity, kmeans
Shawn Xiong Wang, Syed Raza Ali Rizvi, "DT-Tree: A Semantic Representation of Scientific Papers", Computer and Information Technology, International Conference on, vol. 00, no. , pp. 1280-1284, 2010, doi:10.1109/CIT.2010.231
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