Geodesic
Integrating Instance-level and Attribute-level Knowledge into Document Clustering
Computer Science and Information Systems, Tome 8 (2011) no. 3.

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In this paper, we present a document clustering framework incorporating instance-level knowledge in the form of pairwise constraints and attribute-level knowledge in the form of keyphrases. Firstly, we initialize weights based on metric learning with pairwise constraints, then simultaneously learn two kinds of knowledge by combining the distance-based and the constraint-based approaches, finally evaluate and select clustering result based on the degree of users’ satisfaction. The experimental results demonstrate the effectiveness and potential of the proposedmethod.
Keywords: document clustering, pairwise constraints, keyphrases 
@article{CSIS_2011_8_3_a6,
     author = {Jinlong Wang and Shunyao Wu and Gang Li and Zhe Wei},
     title = {Integrating {Instance-level} and {Attribute-level} {Knowledge} into {Document} {Clustering}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {8},
     number = {3},
     year = {2011},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2011_8_3_a6/}
}
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Jinlong Wang; Shunyao Wu; Gang Li; Zhe Wei. Integrating Instance-level and Attribute-level Knowledge into Document Clustering. Computer Science and Information Systems, Tome 8 (2011) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2011_8_3_a6/