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.
Keywords for this software
References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- 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)
- Veale, Tony; Li, Guofu: Analogy as an organizational principle in the construction of large knowledge-bases (2014) ioport
- Gleich, David F.; Wang, Ying; Meng, Xiangrui; Ronaghi, Farnaz; Gerritsen, Margot; Saberi, Amin: Some computational tools for digital archive and metadata maintenance (2011)
- Malik, Hassan H.; Kender, John R.; Fradkin, Dmitriy; Moerchen, Fabian: Hierarchical document clustering using local patterns (2010) ioport
- Liu, Alexander; Jun, Goo; Ghosh, Joydeep: A self-training approach to cost sensitive uncertainty sampling (2009) ioport
- Hung, Shao-Shin; Liu, Damon Shing-Min: Efficient reduction of access latency through object correlations in virtual environments (2007)
- Peng, Yi; Kou, Gang; Shi, Yong; Chen, Zhengxin: Improving clustering analysis for credit card accounts classification (2005)
- Zhong, Shi; Ghosh, Joydeep: A unified framework for model-based clustering (2004)
- Zhou, HaoFeng; Yuan, QingQing; Cheng, ZunPing; Shi, BaiLe: PHC: A fast partition and hierarchy-based clustering algorithms (2003)
Further publications can be found at: http://glaros.dtc.umn.edu/gkhome/publications