A CLUE for CLUster Ensembles

By Kurt Hornik. 2005

311 citations recorded by the source corpus.

Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. The R package clue provides an extensible computational environment for creating and analyzing cluster ensembles, with basic data structures for representing partitions and hierarchies, and facilities for computing on these, including methods for measuring proximity and obtaining consensus and "secondary" clusterings.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a1f99cb970de71c6bfac7c991c9f5ef4543cb1a3

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Citations in the corpus

A Model of Text for Experimentation in the Social Sciences (2016, methodology): “For both LDA and STM, we match the estimated topics to the simulated parameters using the Hungarian algorithm to maximize the dot product of the true θ and the MAP estimate (Papadimitriou and Steiglitz 1998; Hornik 2005).”
Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R (2017, methodology): “, Jain and Dubes 1988; Kaufman and Rousseeuw 1990) or in the documentation supplied with the packages fpc (Hennig 2014), clue (Hornik 2017) and cluster (Maechler et al.”
Text Mining Infrastructure in R (2008, methodology): “E.g., we interface with the Weka and OpenNLP tool kits, via RWeka (Hornik et al. 2007)—and Snowball (Hornik 2007b) for its stemmers—and openNLP (Feinerer 2007a), respectively.”
CRITICAL ASSESSMENT OF AUTOMATED FLOW CYTOMETRY DATA ANALYSIS TECHNIQUES (2013, methodology): “This is an NP-hard problem (multi-dimensional assignment) so we used a heuristic method 41 that provides approximate solutions for the consensus partition problem, as implemented in the CLUE package 42 .”
Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods (2009, background): “clue (Hornik 2005) pava() internal isotonic regression (mean/median) R”
stm: An R Package for Structural Topic Models (2019, unknown): “2012), clue (Hornik 2005), wordcloud (Fellows 2014), KernSmooth (Wand 2015), geometry (Habel et al.”
Cell-to-cell expression variability followed by signal reinforcement progressively segregates early mouse lineages (2013, methodology): “> par(mfrow = c(length(numberFeatures),2)) > colores = sampleColourMap[levels(x$sampleGroup)] > for(i in seq(along = numberFeatures)) { + for(what in c("JSD", "SDV")){ + obj = get(paste("boot", what, sep = "")) + boxplot(obj$t[, , i], main = sprintf("numberFeatures=%d", numberFeatures[i]), + col = c”
NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set (2014, background): “…(MacQueen 1967; Hartigan and Wong 1979) is one of the most popularly adopted partitioning algorithms, as evidenced by its use in a wide variety of packages in the R system for statistical computing, such as cclust (Dimitriadou 2014), clustTool (Templ 2007), clue (Hornik 2005, 2014), among others.”
topicmodels: An R Package for Fitting Topic Models (2011, methodology): “The topics are matched based on the Hellinger distance between the term distributions of the topics using package clue (Hornik 2005).”