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Hierarchical Clustering Algorithms for Document Datasets
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Cited by
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Yang, L., & Shami, A. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.061 · Cited by 3,232
Text Mining Infrastructure in R
Feinerer, I., Hornik, K., & Meyer, D. (2008) · Journal of Statistical Software · doi:10.18637/jss.v025.i05 · Cited by 1,252
Comprehensive survey on hierarchical clustering algorithms and the recent developments
Ran, X., Xi, Y., Lu, Y., Wang, X., & Lu, Z. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10366-3 · Cited by 361
A Deep Matrix Factorization Method for Learning Attribute Representations
Trigeorgis, G., Bousmalis, K., Zafeiriou, S., & Schuller, B. W. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2554555 · Cited by 356
Information retrieval and text mining technologies for chemistry
Krallinger, M., Rabal, O., Lourenço, A., Oyarzabal, J., & Valencia, A. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00851 · Cited by 281
Metadata
- Authors
- Zhao, Y., Karypis, G., & Fayyad, U.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2005-03
- Volume
- 10
- Issue
- 2
- Pages
- 141-168
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2066680326
- Cited by
- 627
- References
- 0
- FWCI
- 35.70
- Type
- book chapter
- Language
- en
- Open access
- green
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- Zhao, Y., Karypis, G., & Fayyad, U. (2005). Hierarchical Clustering Algorithms for Document Datasets. Data Mining and Knowledge Discovery. https://doi.org/10.1007/s10618-005-0361-3
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
