CLUTO - A Clustering Toolkit

By George Karypis. 2002

598 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/345807c4b360d4bb1f04792bb279bf0487e6f33e

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Citations in the corpus

Fuzzy c-Means Algorithms for Very Large Data (2012, methodology): “Other algorithms in this category include GARDEN H D [29] and CLUTO [30], which were designed to address high-dimensional data.”
Quantifying Controversy in Social Media (2015, methodology): “in the whole topic, after standard preprocessing used in IR (lowercasing, stopword removal, stemming). Following the lines of our main pipeline, we group these vectors in two clusters by using CLUTO [Karypis 2002] with cosine distance. The underlying assumption is that the two sides, while sharing t”
Frequent sub-structure-based approaches for classifying chemical compounds (2005, methodology): “To ensure that we get the largest possible clusters we use the maximum-link criterion function for deciding which two clusters should be merged [15].”
Multitask Bregman clustering (2010, methodology): “We use a set of text document collections which are widely used to evaluate clustering algorithms [40,41,36].”
The structure and infrastructure of the global nanotechnology literature (2006, methodology): “The clustering approach presented in this section is based on a partitional clustering algorithm (Zhao & Karypis, 2004; Karypis, 2005 ) contained within a software package named CLUTO.”
BitShred: feature hashing malware for scalable triage and semantic analysis (2011, methodology): “We use graphviz and Cluto [24] for visualizing the clustering and family trees generated as shown in Figure 10, 11.”
Multimodal Distributional Semantics (2013, methodology): “For both sets, following the original proponents and others, we cluster the words based on their pairwise cosines in the semantic space defined by a model using the CLUTO toolkit (Karypis, 2003).”
A Unified Framework for Model-based Clustering (2003, methodology): “2 Datasets We used the 20-newsgroups dataset6 and a number of datasets from the CLUTO toolkit7 (Karypis, 2002).”
Industry Induces Academic Science to Know Less about More1 (2010, methodology): “First, terms were clustered by repeated bisection, the most efficient method for clustering complex data sets (Zhao and Karypis 2005a) as implemented in CLUTO (Karypis 2002).”
Jointly optimizing word representations for lexical and sentential tasks with the C-PHRASE model (2015, methodology): “We feed the similarity matrix produced by a model for all concepts in a test set to the CLUTO toolkit (Karypis, 2003), that clusters them into n groups,”
Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding (2012, methodology): “Both these tests were performed with the Cluto clustering package (Karypis, 2003) and cosine distances, and success was measured as percentage purity over clusters based on their plurality class.”
Efficient retrieval of recommendations in a matrix factorization framework (2012, methodology): “We used the Cluto clustering toolkit [10] for spherical clustering of the user vectors.”
Frequent substructure-based approaches for classifying chemical compounds (2003, methodology): “To ensure that we get the largest possible clusters we use the maximum-link criterion function for deciding which two clusters should be merged [21].”
TOPSIG: topology preserving document signatures (2011, methodology): “This approach is compared to the implementation of k-means in the CLUTO clustering toolkit [10] that is popular in the IR community.”
Nouns are Vectors, Adjectives are Matrices: Representing Adjective-Noun Constructions in Semantic Space (2010, methodology): “Cluster quality is evaluated by percentage purity (Zhao and Karypis, 2003).”
Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors (2014, methodology): “into n groups (with n determined by the gold standard partition) using the CLUTO toolkit (Karypis, 2003), with the repeated bisections with global optimization method and CLUTO’s default settings otherwise (these are standard choices in the literature).”
Distributional Memory: A General Framework for Corpus-Based Semantics (2010, unknown): “nouns are clustered with CLUTO (Karypis 2003), using their similarity matrix based on pairwise cosines repeated bisections algorithm with global optimization method (parameters with default values in CLUTO) cluster quality is evaluated by percentage purity (Zhao & Karypis 2001) Purity = 1 n Pk r=1 m”
Hierarchical Clustering Algorithms for Document Datasets (2005, methodology): “The datasets as well as the various algorithms are available in the CLUTO clustering toolkit [22], which can be downloaded from http://www.”