gCLUTO: An Interactive Clustering, Visualization, and Analysis System

Matt Rasmussen and George Karypis
UMN-CS TR-04-021, 2004
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Clustering algorithms are exploratory data analysis tools that have proved to be essential for gaining valuable insights on various aspects and relationships of the underlying systems. In this paper we present gCLUTO, a stand-alone clustering software package which serves as an easy-to-use platform that combines clustering algorithms along with a number of analysis, reporting, and visualization tools to aid in interactive exploration and clustering-driven analysis of large datasets. gCLUTO provides a wide-range of algorithms that operate either directly on the original feature-based representation of the objects or on the object-to-object similarity graphs and are capable of analyzing different types of datasets and finding clusters with different characteristics. In addition, gCLUTO implements a project-oriented work-flow that eases the process of data analysis.
The software can be obtained from [hlink:[cluto/gcluto/overview][gCLUTO]].
Research topics: Clustering | Data mining | gCLUTO