2024-03-29T15:37:53Z
https://nagoya.repo.nii.ac.jp/oai
oai:nagoya.repo.nii.ac.jp:00005904
2023-01-16T05:11:46Z
435:664:733
Novelty-based Incremental Document Clustering for On-line Documents
Khy, Sophoin
Ishikawa, Yoshiharu
Kitagawa, Hiroyuki
open access
Copyright (c) 2006 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.
Document clustering has been used as a core technique in managing vast amount of data and providing needed information. In on-line environments, generally new information gains more interests than old one. Traditional clustering focuses on grouping similar documents into clusters by treating each document with equal weight. We proposed a novelty-based incremental clustering method for on-line documents that has biases on recent documents. In the clustering method, the notion of 'novelty' is incorporated into a similarity function and a clustering method, a variant of the K-means method, is proposed. We examine the efficiency and behaviors of the method by experiments.
IEEE
2006-04
eng
journal article
VoR
http://hdl.handle.net/2237/7520
https://nagoya.repo.nii.ac.jp/records/5904
https://doi.org/10.1109/ICDEW.2006.100
0-7695-2571-7
Proceedings of the 22nd International Conference on Data Engineering Workshops (ICDEW'06)
40
40
https://nagoya.repo.nii.ac.jp/record/5904/files/wiri2006.pdf
application/pdf
355.0 kB
2018-02-19