wine

<p>These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from
three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three
types of wines.</p>

Implementation of the bootstrapping approach for the estimation of clustering stability and its application in estimating the number of clusters, as introduced by Yu et al (2016)<doi:10.1142/9789814749411_0007>. Implementation of the non-parametric bootstrap approach to assessing the stability of module detection in a graph, the extension for the selection of a parameter set that defines a graph from data in a way that optimizes stability and the corresponding visualization functions, as introduced by Tian et al (2021) <doi:10.1002/sam.11495>. Implemented out-of-bag stability estimation function and k-select Smin-based k-selection function as introduced by Liu et al (2022) <doi:10.1002/sam.11593>. Implemented ensemble clustering method based-on k-means clustering method, spectral clustering method and hierarchical clustering method.

Tianmou Liu

bootcluster

Bootstrapping Estimates of Clustering Stability

Han Yu

Mingmei Tian

wine function

<p>The data set <code>wine</code> contains a <code>data.frame</code> of 14 variables. The first variable is the types
of wines. The other 13 variables are quantities of the constituents.</p>

wine: Wine Data Set

Description

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References