Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from SIGIR 2026 / reviewers in the wild / expert

Yan Zhu 0012

dblp:82/3167-12 · DBLP profile ↗
← Back
3ranked-venue papers
3as first author
0since 2021 · last 2013
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 3 · 3 first-authorArtificial intelligence and machine learning · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining › clustering
constrained clustering		 0.112011
Text Clustering via Constrained Nonnegative Matrix Factorization · ICDM 2011
Data mining › clustering
document clustering		 0.112011
Text Clustering via Constrained Nonnegative Matrix Factorization · ICDM 2011
Data mining › clustering › matrix factorization-based clustering
non-negative matrix factorization clustering		 0.112011
Text Clustering via Constrained Nonnegative Matrix Factorization · ICDM 2011
Data mining
semi-supervised learning		 0.012011
Text Clustering via Constrained Nonnegative Matrix Factorization · ICDM 2011
Data mining
text mining		 0.012011
Text Clustering via Constrained Nonnegative Matrix Factorization · ICDM 2011

Methods — techniques the papers use, named apart from their topics

non-negative matrix factorization · 0.1must-link and cannot-link constraints · 0.1
YearPublicationVenuePosition
2013 A novel semi-supervised learning framework with simultaneous text representing
Yan Zhu 0012, Jian Yu 0001, Liping Jing
 Knowl. Inf. Syst.1
2011 Text Clustering via Constrained Nonnegative Matrix Factorization
abstract
Semi-supervised nonnegative matrix factorization (NMF)receives more and more attention in text mining field. The semi-supervised NMF methods can be divided into two types, one is based on the explicit category labels, the other is based on the pair wise constraints including must-link and cannot-link. As it is hard to obtain the category labels in some tasks, the latter one is more widely used in real applications. To date, all the constrained NMF methods treat the must-link and cannot-link constraints in a same way. However, these two kinds of constraints play different roles in NMF clustering. Thus a novel constrained NMF method is proposed in this paper. In the new method, must-link constraints are used to control the distance of the data in the compressed form, and cannot-ink constraints are used to control the encoding factor. Experimental results on real-world text data sets have shown the good performance of the proposed method.
Yan Zhu 0012, Liping Jing, Jian Yu 0001
 ICDM1
2009 New Labeling Strategy for Semi-supervised Document Categorization
Yan Zhu 0012, Liping Jing, Jian Yu 0001
 KSEM1