README.md
In trevorki/kmeaningfulR: Performs Kmeans Clustering

KmeaningfulR

Have you ever encountered a dataset that seems to have different patterns in it? Have you ever tried to group similar things together in a dataset and to assign a new sample based on your findings?

We created kmeaningfulR to help solve such problems. kmeaningfulR is a R package that uses the k-means algorithm to find clusters and assign new data points to them. It also contains functions to help with data preprocessing, hyperparameter tuning and visualizing clusters.

You can install the released version of kmeaningfulR from github with

install_github("UBC-MDS/kmeaningfulR")

There already exist several packages that implement k-means clustering in R. Most notably there is the stats::kmeans implementation. There are also several functions for modified versions of k-means available in the ClusterR package. We are not trying to break new ground with kmeaningfulR, but rather to build a simple and lightweight implementation from scratch.

preprocess(X) - Automatic dataset preprocessing: scales numerical features
find_elbow(X) - Automatic hyperparameter tuning to select optimal number of clusters, k, according to silhouette score
fit_assign(X, k) - Wrapper function that calls fit(X, k) and assign(X, centres)
- fit(X, k) - finds centroid location for all of the k clusters
- assign(X, centres) - assigns each example to a cluster
show_clusters(X, centres) - Visualize clusters according to 2d PCA representation

R 4.0.3
cluster 2.1.1
FactoMineR
forcats
dplyr

library(kmeaningfulR)

# helper data X
X  <- array(c(0, 1, 10, 10, 10, 10, 0, 1, 10, 11, 0, 1), dim = c(6,2))
# scale data
X_scaled <- preprocess(X)
# find optimal number of clusters
optimal_K <- find_elbow(X_scaled)
# find location of cluster centers
centers <- fit(X_scaled, optimal_K)
# assign label of nearest center to every point
labels <- assign(X_scaled,centers)
# plot a 2D PCA visualisation of the points clusters
show_clusters(X_scaled, labels, centers)

| Task | Function | |------------------------------------|-----------------------------| | Scale numerical features | preprocess(df) | | Find list of centroid points | fit(df, 3) | | Assign new data point to cluster | assign(df, array2d) | | Find optimal number of cluster | fit_elbow(X) | | Visualize data coloured by cluster | show_cluster(df, array2d) |

sample plot