Karypis, George2020-09-022020-09-022002-04-29https://hdl.handle.net/11299/215521Clustering algorithms divide data into meaningful or useful groups, called clusters, such that the intra-cluster similarity is maximized and the inter-cluster similarity is minimized. These discovered clusters can be used to explain the characteristics of the underlying data distribution andthus serve as the foundation for various data mining and analysis techniques. The applications of clustering include characterization of different customer groups based upon purchasing patterns, categorization of documents onthe World Wide Web, grouping of genes and proteins that have similar functionality, grouping of spatial locations prone to earth quakes from seismological data, etc. CLUTO is a software package for clustering low and high dimensional datasets and for analyzing the characteristics of the various clusters. CLUTO provides three different classes of clustering algorithms thatoperate either directly in the object's feature space or in the object'ssimilarity space. These algorithms are based on the partitional, agglomerative, and graph-partitioning paradigms. A key feature in most of CLUTO's clustering algorithms is that they treat the clustering problem as an optimization process which seeks to maximize or minimizea particular clustering criterion function defined either globally or locally over the entire clustering solution space. CLUTO provides a total of seven different criterion functions that can be used to drive both partitional and agglomerative clustering algorithms. Most of these criterion functions have been shown to produce high qualityclustering solutions in high dimensional datasets, especially those arising in document clustering. In addition to these criterion functions, CLUTO providessome of the more traditional local criteria (e.g., single-link, complete-link, and UPGMA) that can be used in the context of agglomerative clustering. Furthermore, CLUTO provides graph-partitioning-based clustering algorithms that are well-suited for finding clusters that form contiguous regions that span different dimensions of the underlying feature space. CLUTO's distribution consists of both stand-alone programs for clustering and analyzing these clusters, as well as, a library via which anapplication program can access directly the various clustering and analysis algorithms implemented in CLUTO.en-USReport