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» Data Clustering: A Review
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IPM
2006
151views more  IPM 2006»
13 years 7 months ago
Document clustering using nonnegative matrix factorization
A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank no...
Farial Shahnaz, Michael W. Berry, V. Paul Pauca, R...
WWW
2008
ACM
14 years 8 months ago
Hidden sentiment association in chinese web opinion mining
The boom of product review websites, blogs and forums on the web has attracted many research efforts on opinion mining. Recently, there was a growing interest in the finergrained ...
Qi Su, Xinying Xu, Honglei Guo, Zhili Guo, Xian Wu...
ICAIL
2005
ACM
14 years 1 months ago
Effective Document Clustering for Large Heterogeneous Law Firm Collections
Computational resources for research in legal environments have historically implied remote access to large databases of legal documents such as case law, statutes, law reviews an...
Jack G. Conrad, Khalid Al-Kofahi, Ying Zhao, Georg...
SDM
2007
SIAM
117views Data Mining» more  SDM 2007»
13 years 9 months ago
Summarizing Review Scores of "Unequal" Reviewers
A frequently encountered problem in decision making is the following review problem: review a large number of objects and select a small number of the best ones. An example is sel...
Hady Wirawan Lauw, Ee-Peng Lim, Ke Wang
BMCBI
2006
119views more  BMCBI 2006»
13 years 7 months ago
LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates
Background: Non-negative matrix factorisation (NMF), a machine learning algorithm, has been applied to the analysis of microarray data. A key feature of NMF is the ability to iden...
Guoli Wang, Andrew V. Kossenkov, Michael F. Ochs