clust.suite: Determination of Suitable Clustering Algorithm for...

In metaCluster: Metagenomic Clustering

Determination of Suitable Clustering Algorithm for Metagenomics Data

Description

This function will give the best clustering algorithm for a given metagenomics data based on silhouette index for kmeans clustering, kmedoids clustering, fuzzy kmeans clsutering, DBSCAN clustering and hierarchical clsutering.

Usage

clust.suite(data, k, eps, minpts)

Arguments

data	Feature matrix consisting of different genomic features.Each row represents features corresponding to a particular individual or contig and each column represents different genomic features.
k	Optimum number of clusters
eps	Radius value for DBSCAN clustering
minpts	Minimum point value of DBSCAN clustering

Value

kmeans	Output of kmeans clustering
kmedoids	Output of kmedoids clustering
fkmeans	Output of fuzzy kmeans clustering
dbscan	Output of dbscan clustering
hierarchical	Output of hierarchical clustering
silhouette.kmeans	Silhouette plot of kmeans clustering
silhouette.kmedoids	Silhouette plot of kmedoids clustering
silhouette.fkmeans	Silhouette plot of fuzzy kmeans clustering
silhouette.dbscan	Silhouette plot of dbscan clustering
silhouette.hierarchical	Silhouette plot of hierarchical clustering
best.clustering.method	Best clustering algorithm based on silhouette index
silhouette.summary	Average silhouette width of each clustering algorithm

Author(s)

Dipro Sinha <[e-mail]>,Sayanti Guha Majumdar, Anu Sharma, Dwijesh Chandra Mishra

Examples

library(metaCluster)
data(metafeatures)
result <- clust.suite(metafeatures[1:200,],8,0.5,10)

metaCluster documentation built on June 22, 2024, 10:21 a.m.