Table of Contents

## How do you cluster analysis in R studio?

K-means Clustering in R

- Specify the number of clusters required denoted by k.
- Assign points to clusters randomly.
- Find the centroids of each cluster.
- Re-assign points according to their closest centroid.
- Re-adjust the positions of the cluster centroids.
- Repeat steps 4 and 5 until no further changes are there.

**How do you cluster in R?**

K-Means Clustering in R

- The K-means Algorithm:
- Specify the desired number of clusters K: Let us choose k=2 for these 5 data points in 2D space.
- Assign each data point to a cluster: Let’s assign three points in cluster 1 using red colour and two points in cluster 2 using yellow colour (as shown in the image).

### How do I visualize a cluster in R?

Use the ggscatter() R function [in ggpubr] or ggplot2 function to visualize the clusters.

**What is a good silhouette score for clustering?**

The silhouette score falls within the range [-1, 1]. The silhouette score of 1 means that the clusters are very dense and nicely separated. The score of 0 means that clusters are overlapping. The score of less than 0 means that data belonging to clusters may be wrong/incorrect.

#### What is Dim1 and Dim2 in cluster plot?

This dimensionality reduction algorithm operates on the four variables and outputs two new variables (Dim1 and Dim2) that represent the original variables, a projection or “shadow” of the original data set. Each dimension represent a certain amount of the variation (i.e. information) contained in the original data set.

**How do you interpret K means cluster analysis?**

It calculates the sum of the square of the points and calculates the average distance. When the value of k is 1, the within-cluster sum of the square will be high. As the value of k increases, the within-cluster sum of square value will decrease.

## What is cluster package?

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).

**How do you analyze a cluster plot?**

The hierarchical cluster analysis follows three basic steps: 1) calculate the distances, 2) link the clusters, and 3) choose a solution by selecting the right number of clusters. First, we have to select the variables upon which we base our clusters.

### Why clustering is used?

Clustering is an unsupervised machine learning method of identifying and grouping similar data points in larger datasets without concern for the specific outcome. Clustering (sometimes called cluster analysis) is usually used to classify data into structures that are more easily understood and manipulated.