Rankcluster: An R Package for clustering multivariate partial ranking
نویسندگان
چکیده
Rankcluster is the first R package dedicated to ranking data. This package proposes modelling and clustering tools for ranking data, potentially multivariate and partial. Ranking data are modelled by the Insertion Sorting Rank (isr) model, which is a meaningful model parametrized by a central ranking and a dispersion parameter. A conditional independence assumption allows to take into account multivariate rankings, and clustering is performed by the mean of mixtures of multivariate isr model. The clusters parameters (central rankings and dispersion parameters) help the practitioners in the interpretation of the clustering. Moreover, the Rankcluster package provides an estimation of the missing ranking positions when rankings are partial. After an overview of the mixture of multivariate isr model, the Rankcluster package is described and its use is illustrated through two real datasets analysis.
منابع مشابه
Rankcluster: An R package for clustering multivariate partial rankings
Rankcluster is the first R package proposing both modelling and clustering tools for ranking data, potentially multivariate and partial. Ranking data are modelled by the Insertion Sorting Rank (isr) model, which is a meaningful model parametrized by a central ranking and a dispersion parameter. A conditional independence assumption allows to take into account multivariate rankings, and clusteri...
متن کاملmultiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles
Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult...
متن کاملkernlab – An S4 Package for Kernel Methods in R
kernlab is an extensible package for kernel-based machine learning methods in R. It takes advantage of R’s new S4 object model and provides a framework for creating and using kernelbased algorithms. The package contains dot product primitives (kernels), implementations of support vector machines and the relevance vector machine, Gaussian processes, a ranking algorithm, kernel PCA, kernel CCA, k...
متن کاملModel-based clustering for multivariate partial ranking data
This paper proposes the first model-based clustering algorithm dedicated to multivariate partial ranking data. This is an extension of the Insertion Sorting Rank (isr) model for ranking data, which is a meaningful and effective model obtained by modelling the ranking generating process assumed to be a sorting algorithm. The heterogeneity of the rank population is modelled by a mixture of isr, w...
متن کاملMultivariate normal mixture modeling, clustering and classification with the rebmix package
The rebmix package provides R functions for random univariate and multivariate finite mixture model generation, estimation, clustering and classification. The paper is focused on multivariate normal mixture models with unrestricted variance-covariance matrices. The objective is to show how to generate datasets for a known number of components, numbers of observations and component parameters, h...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2013