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briKmeans (version 1.0)

Package for Brik, Fabrik and Fdebrik Algorithms to Initialise Kmeans

Description

Implementation of the BRIk, FABRIk and FDEBRIk algorithms to initialise k-means. These methods are intended for the clustering of multivariate and functional data, respectively. They make use of the Modified Band Depth and bootstrap to identify appropriate initial seeds for k-means, which are proven to be better options than many techniques in the literature. Torrente and Romo (2021) It makes use of the functions kma and kma.similarity, from the archived package fdakma, by Alice Parodi et al.

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Version

Install

install.packages('briKmeans')

Monthly Downloads

199

Version

1.0

License

GPL (>= 3)

Maintainer

Aurora Torrente

Last Published

July 21st, 2022

Functions in briKmeans (1.0)

fdebrik

Computation of Initial Seeds for Kmeans with a Functional Extension of Brik
kma

Clustering and alignment of functional data
kma.similarity

Similarity/dissimilarity index between two functions
brik

Computation of Initial Seeds and Kmeans Results
plotKmeansClustering

Kmeans Clustering Plot
fabrik

Computation of Initial Seeds for Kmeans and Clustering of Functional Data
elbowRule

Selection of Appropriate DF Parameter Based on an Elbow Rule for the Distortion