UNSUPERVISED LEARNING-CLUSTER ANALYSIS IN R

Unsupervised learning is a type of machine learning where the model is trained on unlabeled data, without a specific target variable or output. The goal of unsupervised learning is to uncover hidden patterns or structure in the data, and it is used for tasks such as clustering, anomaly detection, and dimensionality reduction.

Some common unsupervised learning techniques include:

• Clustering: grouping similar data points together.
• Dimensionality reduction: reducing the number of features in the data while preserving the most important information.
• Anomaly detection: identifying data points that are unusual or different from the others.

Unlike supervised learning, unsupervised learning does not have a clear goal or objective to optimize for, so the evaluation of unsupervised learning models can be more challenging.

Unsupervised learning is widely used in many fields, such as natural language processing, computer vision, and bioinformatics. It can be used to analyze customer data to identify segments for targeted marketing, to identify patterns in financial data, or to organize scientific data into coherent groups for further study.

Cluster analysis is a method of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). R is a popular programming language for data analysis and statistics, and there are many packages available for performing cluster analysis in R. Some popular packages for cluster analysis in R include:

• cluster: Basic functions for cluster analysis including k-means, hierarchical clustering, and model-based clustering.
• fpc: A collection of clustering methods including k-means and hierarchical clustering.
• mclust: Model-based clustering for Gaussian finite mixture models.
• dbscan: Density-based clustering using the DBSCAN algorithm.
• factoextra: A package for performing cluster analysis and visualization in R.

To perform cluster analysis in R, you will first need to load the appropriate package and then call the relevant function to perform the analysis on your dataset. You can use the result of the analysis to interpret the clusters and gain insights about your data.

The cluster package in R is a popular package for performing cluster analysis. It provides a variety of functions for clustering data, including k-means, hierarchical clustering, and model-based clustering. Here are a few examples of how to use the cluster package to perform clustering in R:

👉K-means Clustering:

Visualizing the results of k-means clustering can help you understand the structure of your data and the cluster assignments for each data point. Here are a few examples of how to visualize k-means clustering results in R:

# Perform k-means clustering on the iris dataset with 3 clusters kmeans_result <- kmeans(iris[, 1:4], 3) # Create a scatter plot of the first two features with the cluster assignments as the color library(ggplot2) ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = kmeans_result$cluster)) + geom_point()

x=iris[,3:4] #using only petal length and width columns

model=kmeans(x,3)

library(cluster)

clusplot(x,model$cluster)

library(tidyverse)

library(cluster)

library(factoextra)

library(gridExtra)

data('USArrests') 

d_frame <- USArrests

d_frame <- na.omit(d_frame)  #Removing the missing values

d_frame <- scale(d_frame)

kmeans2 <- kmeans(d_frame, centers = 2, nstart = 25)

kmeans3 <- kmeans(d_frame, centers = 3, nstart = 25) 

kmeans4 <- kmeans(d_frame, centers = 4, nstart = 25)  

kmeans5 <- kmeans(d_frame, centers = 5, nstart = 25)  

#Comparing the Plots

plot1 <- fviz_cluster(kmeans2, geom = "point", data = d_frame) + ggtitle("k = 2")

plot2 <- fviz_cluster(kmeans3, geom = "point", data = d_frame) + ggtitle("k = 3")

plot3 <- fviz_cluster(kmeans4, geom = "point", data = d_frame) + ggtitle("k = 4")

plot4 <- fviz_cluster(kmeans5, geom = "point", data = d_frame) + ggtitle("k = 5")

grid.arrange(plot1, plot2, plot3, plot4, nrow = 2)

👉Parallel Coordinates Plot:

This will create a parallel coordinates plot of all the features of the iris dataset, with the lines colored by the cluster assignment. This is useful when the data has more than 2 features and you want to see the distribution of each feature in each cluster.

>ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = factor(cluster))) +

 geom_point()

👉Principal Component Analysis (PCA) Plot:

# Perform PCA on the iris dataset pca_result <- prcomp(iris[, 1:4]) # Create a scatter plot of the first two principal components with the cluster assignments as the color ggplot(data.frame(pca_result$x[, 1:2], cluster = kmeans_result$cluster), aes(x = PC1, y = PC2, color = cluster)) + geom_point()

This will create a scatter plot of the first two principal components of the iris dataset, which can help to reduce the dimensionality of the data and make it easier to visualize.

These are just a few examples of how to visualize k-means clustering results in R, and there are many other options available depending on the structure of your data and the information you want to convey.

👉hierarchical clustering

There are several packages in R that can be used to perform hierarchical clustering and create visualizations of the resulting dendrograms. One popular package is the "dendextend" package which provides various functions for visualizing dendrograms. Here's an example of how you can use the package to create a dendrogram of a hierarchical clustering solution:

# install and load the dendextend package

install.packages("dendextend")

library(dendextend)

# generate example data

set.seed(123)

data <- matrix(rnorm(50*2), ncol=2)

# perform hierarchical clustering

hc <- hclust(dist(data))

# create dendrogram object

dend <- as.dendrogram(hc)

# plot the dendrogram

plot(dend)