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 11 articles )

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  1. 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)
  2. Kurt Hornik; Bettina Grün: movMF: An R Package for Fitting Mixtures of von Mises-Fisher Distributions (2014)
  3. Veale, Tony; Li, Guofu: Analogy as an organizational principle in the construction of large knowledge-bases (2014) ioport
  4. Kurt Hornik; Ingo Feinerer; Martin Kober; Christian Buchta: Spherical k-Means Clustering (2012)
  5. Gleich, David F.; Wang, Ying; Meng, Xiangrui; Ronaghi, Farnaz; Gerritsen, Margot; Saberi, Amin: Some computational tools for digital archive and metadata maintenance (2011)
  6. Malik, Hassan H.; Kender, John R.; Fradkin, Dmitriy; Moerchen, Fabian: Hierarchical document clustering using local patterns (2010) ioport
  7. Liu, Alexander; Jun, Goo; Ghosh, Joydeep: A self-training approach to cost sensitive uncertainty sampling (2009) ioport
  8. Hung, Shao-Shin; Liu, Damon Shing-Min: Efficient reduction of access latency through object correlations in virtual environments (2007)
  9. Peng, Yi; Kou, Gang; Shi, Yong; Chen, Zhengxin: Improving clustering analysis for credit card accounts classification (2005)
  10. Zhong, Shi; Ghosh, Joydeep: A unified framework for model-based clustering (2004)
  11. Zhou, HaoFeng; Yuan, QingQing; Cheng, ZunPing; Shi, BaiLe: PHC: A fast partition and hierarchy-based clustering algorithms (2003)

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