CLUTO is a software package for clustering low- and high-dimensional datasets and for analyzing the characteristics of the various clusters. CLUTO is well-suited for clustering data sets arising in many diverse application areas including information retrieval, customer purchasing transactions, web, GIS, science, and biology.CLUTO’s distribution consists of both stand-alone programs and a library via which an application program can access directly the various clustering and analysis algorithms implemented in CLUTO.

References in zbMATH (referenced in 18 articles )

Showing results 1 to 18 of 18.
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  1. Teboulle, Marc; Vaisbourd, Yakov: Novel proximal gradient methods for nonnegative matrix factorization with sparsity constraints (2020)
  2. François Role, Stanislas Morbieu, Mohamed Nadif: CoClust: A Python Package for Co-Clustering (2019) not zbMATH
  3. Śmieja, Marek; Hajto, Krzysztof; Tabor, Jacek: Efficient mixture model for clustering of sparse high dimensional binary data (2019)
  4. Huang, Shudong; Wang, Hongjun; Li, Tao; Li, Tianrui; Xu, Zenglin: Robust graph regularized nonnegative matrix factorization for clustering (2018)
  5. Du, Rundong; Kuang, Da; Drake, Barry; Park, Haesun: DC-NMF: nonnegative matrix factorization based on divide-and-conquer for fast clustering and topic modeling (2017)
  6. Kirill Efimov, Larisa Adamyan, Vladimir Spokoiny: Adaptive Nonparametric Clustering (2017) arXiv
  7. Kurt Hornik; Bettina Grün: movMF: An R Package for Fitting Mixtures of von Mises-Fisher Distributions (2014) not zbMATH
  8. Veale, Tony; Li, Guofu: Analogy as an organizational principle in the construction of large knowledge-bases (2014) ioport
  9. Kurt Hornik; Ingo Feinerer; Martin Kober; Christian Buchta: Spherical k-Means Clustering (2012) not zbMATH
  10. Priebe, Carey E.; Solka, Jeffrey L.; Marchette, David J.; Bryant, Avory C.: Quantitative horizon scanning for mitigating technological surprise: detecting the potential for collaboration at the interface (2012)
  11. Gleich, David F.; Wang, Ying; Meng, Xiangrui; Ronaghi, Farnaz; Gerritsen, Margot; Saberi, Amin: Some computational tools for digital archive and metadata maintenance (2011)
  12. Malik, Hassan H.; Kender, John R.; Fradkin, Dmitriy; Moerchen, Fabian: Hierarchical document clustering using local patterns (2010) ioport
  13. Liu, Alexander; Jun, Goo; Ghosh, Joydeep: A self-training approach to cost sensitive uncertainty sampling (2009)
  14. Hung, Shao-Shin; Liu, Damon Shing-Min: Efficient reduction of access latency through object correlations in virtual environments (2007)
  15. Peng, Yi; Kou, Gang; Shi, Yong; Chen, Zhengxin: Improving clustering analysis for credit card accounts classification (2005)
  16. Wang, Jason T. L.; Zaki, Mohammed J.; Toivonen, Hannu T. T.; Shasha, Dennis: Data mining in bioinformatics (2005)
  17. Zhong, Shi; Ghosh, Joydeep: A unified framework for model-based clustering (2004)
  18. Zhou, HaoFeng; Yuan, QingQing; Cheng, ZunPing; Shi, BaiLe: PHC: A fast partition and hierarchy-based clustering algorithms (2003)

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