In UBC-MDS/ssgkmeansr: An R package for k-means clustering

Description

Build clusters and save cluster attributes with random selection or k-means ++ centroid initialization. Returns a list containing: 1. data frame of the attributes and clustering for each data point; 2. total within cluster sum of square; 3. data frame of k centroids.

Usage

fit(data, K, method)

Arguments

data Data frame. Attributes as columns and data points as rows k Intger. Number of clusters. method String. Centroid initialization method. random or kmpp

Examples

fit(my_data_frame,3,"kmpp")

Details

This package implements the classical unsupervised clustering method, k-means, with options for choosing the initial centroids (e.g. random and kmeans++). Users will be able to find clusters in their data, label new data, and observe the clustering results.

UBC-MDS/ssgkmeansr documentation built on May 25, 2019, 1:36 p.m.