Hierarchical Clustering Algorithms for Document Datasets
By Ying Zhao, George Karypis, Usama M. Fayyad. Data mining and knowledge discovery, 2005
734 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing
View Paper: https://www.semanticscholar.org/paper/df71e6e1e5d433964e18a7f6d422f8a9907568c2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Kurt Hornik
- David Meyer
- Ingo Feinerer
- Björn Schuller
- Stefanos P. Zafeiriou
- Konstantinos Bousmalis
- Hendrik Purwins
- Alberto Barrón-Cedeño
In Statistical and Nonlinear Physics
- Hendrik Purwins
- Changshui Zhang
- Xiangwen Wang
- Stefanos P. Zafeiriou
- Zhenyu Lu
- Jianwen Zhang
- Ricard Marxer
- Yue Xi
In Signal Processing
- David Meyer
- Ingo Feinerer
- George Trigeorgis
- Stefanos P. Zafeiriou
- Björn Schuller
- Hendrik Purwins
- Changshui Zhang
- Xiangwen Wang
Citations in the corpus
- Unsupervised Incremental Online Learning and Prediction of Musical Audio Signals (2015, methodology): “Although this assumption will not always be true in our evaluation, we will use ARI, since it is a more established measure than alternative ones, such as the Fowlkes-Mallows index, the Mirkin metric, the Jaccard index [41], or entropy-based measures [37], [39], e.”
- A Deep Matrix Factorization Method for Learning Attribute Representations (2015, methodology): “By initializing a Deep Semi-NMF with the weights learned with Deep WSF from the small labelled dataset we can leverage all the information we have and allow our unsupervised model to uncover better representations for our initial data on the task of clustering faces.”
- Multitask Bregman clustering (2010, methodology): “We use a set of text document collections which are widely used to evaluate clustering algorithms [40,41,36].”
- Text Mining Infrastructure in R (2008, background): “Classical applications in text mining (Weiss et al. 2004) come from the data mining community, like document clustering (Zhao and Karypis 2005b,a; Boley 1998; Boley et al. 1999) and document classification (Sebastiani 2002).”
- Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, methodology): “Later, Zhao et al. (2005) proposed a Chameleon-like algorithm to cluster the documents (high-dimensional data).”
- An Evaluation Framework for Plagiarism Detection (2010, methodology): “To this end, the source documents and the suspicious documents were clustered into k = 30 clusters using bisecting k - means [22].”