Cluto is software package intended for clustering low- and high-dimensional datasets and for analyzing the characteristics of the various clusters. It is well-suited for clustering data sets, arisen 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. This software has several features such as multiple classes of clustering algorithms – partitional, agglomerative, & graph-partitioning based; multiple similarity/distance functions – Euclidean distance, cosine, correlation coefficient, extended Jaccard, user-defined; numerous novel clustering criterion functions and agglomerative merging schemes;.
Traditional agglomerative merging schemes – single -link, complete-link, UPGMA; extensive cluster visualization capabilities and output options – postscript , SVG, gif, xfig, etc; multiple methods for effectively summarizing the clusters – most descriptive and discriminating dimensions, cliques, and frequent itemsets; can scale to very large datasets containing hundreds of thousands of objects and tens of thousands of dimensions.
This software has a cross-platform graphical application called gCLUTO, for clustering low- and high-dimensional datasets and for analyzing the characteristics of the various clusters. This app is build on-top of the CLUTO clustering library and it provides tools for visualizing the resulting clustering solutions using tree, matrix, and an OpenGL-based mountain visualization.
Cluto also has a web-enabled data clustering application, called wCLUTO, designed for the clustering and data-analysis requirements of gene-expression analysis and it is just like gCLUTO it is build on top of the CLUTO clustering library, so users can upload their datasets, select from a number of clustering methods, perform the analysis on the server, and visualize the final results.