Difference between revisions of "Machine-Learning-using-R - old 2022/C2/Unsupervised-Learning/English"

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Line 23: Line 23:
 
|| In this tutorial, we will learn about:  
 
|| In this tutorial, we will learn about:  
 
* '''Unsupervised Learning ''' and its applications
 
* '''Unsupervised Learning ''' and its applications
−
* '''k-means clustering '''on '''iris''' data set
+
* '''k-means clustering '''on '''iris data set'''
 
* Measure the performance using '''Adjusted RAND Index'''.
 
* Measure the performance using '''Adjusted RAND Index'''.
  
Line 55: Line 55:
 
'''Unsupervised Learning'''
 
'''Unsupervised Learning'''
 
||  
 
||  
−
* It is a technique that is applied on unlabelled datasets.
+
* It is a technique that is applied on unlabelled '''datasets'''.
−
* It uses '''Machine Learning algorithms''' to analyse and cluster unlabelled data.
+
* It uses '''Machine Learning algorithms''' to analyse and cluster unlabelled '''data'''.
−
* It deals with finding groups of data points with similar characteristics.
+
* It deals with finding groups of '''data points''' with similar characteristics.
  
 
|-  
 
|-  
Line 65: Line 65:
 
|| Types of '''Unsupervised learning.'''
 
|| Types of '''Unsupervised learning.'''
  
−
* '''Clustering''': it is used for grouping search engine results.
+
* '''Clustering''': it is used for grouping '''search engine''' results.
 
* '''Anomaly Detection''': It is used to detect fraudulent transactions.
 
* '''Anomaly Detection''': It is used to detect fraudulent transactions.
−
 
  
 
|-  
 
|-  
Line 73: Line 72:
  
 
'''k-means Clustering'''
 
'''k-means Clustering'''
−
|| Now let us implement '''k-means clustering''' on the '''iris''' data set.
+
|| Now let us implement '''k-means clustering''' on the '''iris data set.'''
  
  
Line 81: Line 80:
  
 
'''Download Files'''
 
'''Download Files'''
−
|| For this tutorial, we will use a script file''' Clustering.R'''.
+
|| For this tutorial, we will use a '''script''' file''' Clustering.R'''.
  
  
Line 95: Line 94:
  
  
−
This folder is located inthe '''MLProject''' folder on my Desktop.
+
This folder is located inthe '''MLProject''' folder on my '''Desktop'''.
  
  
−
I have also set the '''UnsupervisedLearning''' folder as my working '''directory'''.
+
I have also set the '''UnsupervisedLearning''' folder as my '''Working directory'''.
 
|-  
 
|-  
 
||  
 
||  
Line 106: Line 105:
  
 
Point to '''Clustering.R''' in''' RStudio.'''
 
Point to '''Clustering.R''' in''' RStudio.'''
−
|| Open the script '''Clustering.R''' in '''RStudio'''.
+
|| Open the '''script Clustering.R''' in '''RStudio'''.
  
  
−
Script '''Clustering.R''' opens in '''RStudio'''.
+
'''Script Clustering.R''' opens in '''RStudio'''.
 
|-  
 
|-  
 
|| [RStudio]
 
|| [RStudio]
Line 120: Line 119:
  
 
Point to the iris data set.
 
Point to the iris data set.
 +
|| Select the given '''commands'''.
  
  
−
|| Select the given commands.
+
Click the '''Run''' button to see the''' iris data table'''.
−
 
+
−
 
+
−
Click the '''Run''' button to see the''' iris''' data table.
+
 
|-  
 
|-  
 
|| [RStudio]
 
|| [RStudio]
Line 136: Line 133:
  
 
Highlight 3 species
 
Highlight 3 species
−
|| Here we are using the labeled''' iris '''data set.  
+
|| Here we are using the labeled''' iris data set'''.  
  
 
It contains 3 species - '''Setosa''', '''Versicolor''' and '''Virginica. '''
 
It contains 3 species - '''Setosa''', '''Versicolor''' and '''Virginica. '''
Line 147: Line 144:
 
'''Posing the Problem '''
 
'''Posing the Problem '''
  
−
|| Can we group data based on sepal and petal dimensions?  
+
|| Can we group '''data''' based on '''sepal''' and '''petal''' dimensions?  
  
 
If so, do these groups represent the original species label accurately?
 
If so, do these groups represent the original species label accurately?
Line 161: Line 158:
 
'''Finding Number of Clusters'''
 
'''Finding Number of Clusters'''
 
||
 
||
−
* For real-life unlabelled data, we should find the optimal number of clusters.
+
* For real-life unlabelled '''data''', we should find the optimal number of '''clusters'''.
 
* For this, we use the '''Elbow Method'''.
 
* For this, we use the '''Elbow Method'''.
  
Line 170: Line 167:
 
|-  
 
|-  
 
|| Highlight '''Clustering.R''' in the '''Source''' window button
 
|| Highlight '''Clustering.R''' in the '''Source''' window button
−
|| In the '''Source''' window, click on the script '''Clustering.R'''.  
+
|| In the '''Source''' window, click on the '''script Clustering.R'''.  
 
|-  
 
|-  
 
|| [RStudio]
 
|| [RStudio]
Line 181: Line 178:
 
'''install.packages()'''
 
'''install.packages()'''
  
−
|| I will import the necessary packages.
+
|| I will import the necessary '''packages'''.
  
  
−
* '''ggplot2''' to visualize the data  
+
* '''ggplot2''' to visualize the '''data'''
 
* '''mclust''' to measure the accuracy.
 
* '''mclust''' to measure the accuracy.
  
−
As I have already installed these packages, I will directly import them.  
+
As I have already installed these '''packages''', I will directly import them.  
  
  
−
If you have not installed these libraries, please install them using '''install.packages '''function.  
+
If you have not installed these '''libraries''', please install them using '''install.packages function'''.  
  
 
|-  
 
|-  
 
|| Type  
 
|| Type  
−
 
−
 
 
'''library(ggplot2)'''
 
'''library(ggplot2)'''
  
Line 203: Line 198:
 
Press '''Ctrl''' + '''S''' keys to save the script.  
 
Press '''Ctrl''' + '''S''' keys to save the script.  
  
−
|| At the top of the script, type the following commands.  
+
|| At the top of the '''script''', type the following '''commands'''.  
  
  
−
Press '''Ctrl''' + '''S''' keys to save the script.  
+
Press '''Ctrl''' + '''S''' keys to save the '''script'''.  
  
 
|-  
 
|-  
Line 216: Line 211:
  
 
Click the '''Run''' button.
 
Click the '''Run''' button.
−
|| Click the '''Run''' button to load these libraries.  
+
|| Click the '''Run''' button to load these '''libraries'''.  
  
  
Line 227: Line 222:
  
  
−
|| Click on the '''iris '''data set.
+
|| Click on the '''iris data set'''.
  
  
−
For the '''k-means clustering''', we need to select two features of the data set.  
+
For the '''k-means clustering''', we need to select two features of the '''data set'''.  
  
  
−
So, we need to find the features that separate the data points clearly.
+
So, we need to find the features that separate the '''data points''' clearly.
  
  
Line 239: Line 234:
 
* '''Sepal Length''' versus''' Sepal Width''' Width
 
* '''Sepal Length''' versus''' Sepal Width''' Width
 
* '''Petal Length ''' Length versus '''Petal Width'''  
 
* '''Petal Length ''' Length versus '''Petal Width'''  
−
 
  
 
|-  
 
|-  
Line 253: Line 247:
  
 
Click on '''Save''' button.
 
Click on '''Save''' button.
−
|| First, we will plot Sepal Length versus Sepal Width.  
+
|| First, we will plot '''Sepal Length''' versus '''Sepal Width'''.  
  
−
Type the following command.
+
Type the following '''command'''.  
−
 
+
−
Save the script.
+
  
 +
Save the '''script'''.
  
 
|-  
 
|-  
Line 267: Line 260:
  
 
Click the '''Run''' button.
 
Click the '''Run''' button.
−
|| Select this command and run it to get a plot.
+
|| Select this '''command''' and '''run''' it to get a plot.
  
 
|-
 
|-
 
   
 
   
 
|| Drag Boundary
 
|| Drag Boundary
−
|| I will drag the boundary to see the '''Plot '''clearly.
+
|| I will drag the boundary to see the plot clearly.
  
 
|-  
 
|-  
Line 291: Line 284:
 
|-  
 
|-  
  
−
|| Point to '''Petal Length''' versus P'''etal Width.'''
+
|| Point to '''Petal Length''' versus '''Petal Width.'''
  
  
Line 297: Line 290:
  
 
'''ggplot(iris,aes(x = Petal.Length, y = Petal.Width, col= Species)) + geom_point()'''
 
'''ggplot(iris,aes(x = Petal.Length, y = Petal.Width, col= Species)) + geom_point()'''
−
|| Now we will plot '''Petal Length''' versus P'''etal Width.'''
+
|| Now we will plot '''Petal Length''' versus '''Petal Width.'''
  
  
−
Type the following command.
+
Type the following '''command'''.
  
 
|-  
 
|-  
Line 310: Line 303:
  
 
Click on '''Save '''and '''Run '''buttons.
 
Click on '''Save '''and '''Run '''buttons.
−
|| Save and run this command to see the plot.
+
|| Save and '''run''' this '''command''' to see the plot.
  
 
|-  
 
|-  
 
|| Drag boundary to see the plot window clearly.
 
|| Drag boundary to see the plot window clearly.
−
|| Drag the boundary to see the '''Plot '''window clearly.
+
|| Drag the boundary to see the plot window clearly.
  
 
|-  
 
|-  
  
 
|| Highlight Output in '''Plot''' Window
 
|| Highlight Output in '''Plot''' Window
−
|| Notice that '''Petal Length''' and '''Petal Width''' clearly separate the 3 species of iris.  
+
|| Notice that '''Petal Length''' and '''Petal Width''' clearly separate the 3 species of '''iris'''.  
  
  
Line 335: Line 328:
  
 
Highlight '''Species''' column  
 
Highlight '''Species''' column  
−
|| In this data set we have three species of iris.
+
|| In this '''data set''' we have three species of '''iris'''.
  
  
−
In this case we already know that we need three clusters.
+
In this case we already know that we need three '''clusters'''.
  
  
Line 354: Line 347:
 
'''km <- kmeans(iris[,3:4],3,nstart = 20)'''
 
'''km <- kmeans(iris[,3:4],3,nstart = 20)'''
  
−
|| Now let’s use the '''kmeans() '''function to perform '''k-means clustering.'''
+
|| Now let’s use the '''kmeans() function''' to perform '''k-means clustering.'''
  
  
−
Type the following command.
+
Type the following '''command'''.
  
 
|-  
 
|-  
Line 365: Line 358:
  
 
Highlight '''nstart=20'''
 
Highlight '''nstart=20'''
−
|| Let me explain the parameters of this function.
+
|| Let me explain the '''parameters''' of this '''function'''.
  
  
−
This command uses '''Petal Length '''and '''Petal''' '''Width''' for '''k-means''' '''clustering'''.
+
This command uses '''Petal Length '''and '''Petal Width''' for '''k-means clustering'''.
  
  
−
This is used to find the best starting points of the centroids.
+
This is used to find the best starting points of the '''centroids'''.
  
 
|-  
 
|-  
Line 382: Line 375:
 
Highlight km <- kmeans(iris[,3:4],3,nstart = 20)
 
Highlight km <- kmeans(iris[,3:4],3,nstart = 20)
  
−
|| Save the command and run it.
+
|| Save the '''command''' and '''run''' it.
  
  
−
It runs 20 iterations and randomly initializes centroids.
+
It runs 20 iterations and randomly initializes '''centroids'''.
  
  
−
Then it chooses the best configuration of centroids for clustering.
+
Then it chooses the best configuration of '''centroids''' for '''clustering'''.
 
|-  
 
|-  
 
|| [RStudio]
 
|| [RStudio]
Line 397: Line 390:
  
  
−
Since the iris data set used here has labels, we will just tabulate the data.
+
Since the '''iris data set''' used here has labels, we will just tabulate the '''data'''.
  
 
|-  
 
|-  
Line 407: Line 400:
  
 
Click on '''Save '''and '''Run '''buttons.
 
Click on '''Save '''and '''Run '''buttons.
−
|| Type the following command.  
+
|| Type the following '''command'''.  
  
−
Save the script and run the command.
+
Save the '''script''' and '''run''' the '''command'''.
 
|-  
 
|-  
 
|| Drag boundary to see the '''console''' window.
 
|| Drag boundary to see the '''console''' window.
Line 420: Line 413:
  
  
−
Each row contains data points of 1 '''cluster'''.
+
Each row contains '''data points''' of 1 '''cluster'''.
  
  
Line 437: Line 430:
 
Point to Setosa, versicolor and virginica.
 
Point to Setosa, versicolor and virginica.
  
−
|| 50 '''Setosa''' samples have been clustered together.
+
|| 50 '''Setosa''' samples have been '''clustered''' together.
  
  
−
2 '''Versicolor''' samples have been incorrectly clustered.
+
2 '''Versicolor''' samples have been incorrectly '''clustered'''.
  
  
−
4 '''Virginica''' samples have been incorrectly clustered.
+
4 '''Virginica''' samples have been incorrectly '''clustered'''.
  
  
Line 462: Line 455:
 
'''Adjusted Rand Index'''
 
'''Adjusted Rand Index'''
 
||  
 
||  
−
* This is a measure of similarity between two clusters.
+
* This is a measure of similarity between two '''clusters'''.
 
* It ranges from -1 to +1, where -1 is bad and +1 is good.
 
* It ranges from -1 to +1, where -1 is bad and +1 is good.
−
 
  
 
|-  
 
|-  
Line 483: Line 475:
  
  
−
|| '''mclust '''library''' '''contains the function to calculate the '''adjusted RAND index'''.
+
|| '''mclust library''' contains the '''function''' to calculate the '''adjusted RAND index'''.
  
  
−
Type the following command.
+
Type the following '''command'''.
  
 
|-  
 
|-  
  
 
|| Click on '''Save '''and '''Run '''buttons.
 
|| Click on '''Save '''and '''Run '''buttons.
−
|| Save and run the command.
+
|| Save and '''run''' the '''command'''.
  
 
|-  
 
|-  
Line 502: Line 494:
  
  
−
This means that our model has performed very well on this data set.
+
This means that our model has performed very well on this '''data set'''.
  
 
|-  
 
|-  
Line 516: Line 508:
 
|| In this tutorial we have learnt:
 
|| In this tutorial we have learnt:
 
* '''Unsupervised Learning '''and its applications
 
* '''Unsupervised Learning '''and its applications
−
* '''k-means clustering '''on '''iris''' data set
+
* '''k-means clustering '''on '''iris data set'''
 
* Measure the performance using '''Adjusted RAND Index'''.
 
* Measure the performance using '''Adjusted RAND Index'''.
−
 
  
 
|-  
 
|-  
Line 531: Line 522:
 
'''Assignment'''
 
'''Assignment'''
 
||
 
||
−
* Using inbuilt '''PlantGrowth '''data set, perform '''k-means clustering'''.
+
* Using '''inbuilt PlantGrowth data set''', perform '''k-means clustering'''.
 
* Evaluate using '''adjusted RAND index'''.
 
* Evaluate using '''adjusted RAND index'''.
−
 
  
 
|-  
 
|-  

Revision as of 15:34, 22 February 2022

Title of the script: Unsupervised Learning

Author: Tanmay Srinath

Keywords: R, RStudio, machine learning, load libraries, ggplot2, mclust, unsupervised learning, classification, k-means clustering, iris dataset, adjusted rand index, video tutorial.


Visual Cue Narration
Show Slide

Opening Slide

Welcome to this spoken tutorial on Unsupervised Learning.
Show Slide

Learning Objectives

In this tutorial, we will learn about:
  • Unsupervised Learning and its applications
  • k-means clustering on iris data set
  • Measure the performance using Adjusted RAND Index.
Show Slide

System Specifications

This tutorial is recorded using,
  • Ubuntu Linux OS version 20.04
  • R version 4.1.2
  • RStudio version 1.4.1717

It is recommended to install R version 4.1.0 or higher.

Show Slide

Prerequisites

To follow this tutorial, the learner should know:
  • Basics programming in R.
  • Basics of Machine Learning.

If not, please access the relevant tutorials on R on this website.

Let us now learn about Unsupervised learning.
Show Slide

Unsupervised Learning

  • It is a technique that is applied on unlabelled datasets.
  • It uses Machine Learning algorithms to analyse and cluster unlabelled data.
  • It deals with finding groups of data points with similar characteristics.
Show Slide

Types of Unsupervised Learning

Types of Unsupervised learning.
  • Clustering: it is used for grouping search engine results.
  • Anomaly Detection: It is used to detect fraudulent transactions.
Show Slide

k-means Clustering

Now let us implement k-means clustering on the iris data set.


To know more about clustering and its types, please refer to Additional Reading Material.

Show Slide

Download Files

For this tutorial, we will use a script file Clustering.R.


Please download this file from the Code files link of this tutorial.


Make a copy and then use it for practising.

[Computer screen]

Highlight Clustering.R and the folder UnsupervisedLearning

I have downloaded and moved this file to the UnsupervisedLearning folder.


This folder is located inthe MLProject folder on my Desktop.


I have also set the UnsupervisedLearning folder as my Working directory.

Let us switch to RStudio.
Double-click Clustering.R

Point to Clustering.R in RStudio.

Open the script Clustering.R in RStudio.


Script Clustering.R opens in RStudio.

[RStudio]

data(“iris”)

View(“iris”)

Click the Run button.

Point to the iris data set.

Select the given commands.


Click the Run button to see the iris data table.

[RStudio]

Highlight Iris in source window.

Highlight Species column

Scroll the table to show the species.

Highlight 3 species

Here we are using the labeled iris data set.

It contains 3 species - Setosa, Versicolor and Virginica.


There are 50 samples of each species for a total of 150 samples.

Show Slide

Posing the Problem

Can we group data based on sepal and petal dimensions?

If so, do these groups represent the original species label accurately?

Show Slide

Solution

The answer to this problem is to use a clustering algorithm.
Show Slide

Finding Number of Clusters

  • For real-life unlabelled data, we should find the optimal number of clusters.
  • For this, we use the Elbow Method.

To know more about it, please refer to the Additional Reading Material.

Let us switch to Rstudio.
Highlight Clustering.R in the Source window button In the Source window, click on the script Clustering.R.
[RStudio]


ggplot2

mclust

install.packages()

I will import the necessary packages.


  • ggplot2 to visualize the data
  • mclust to measure the accuracy.

As I have already installed these packages, I will directly import them.


If you have not installed these libraries, please install them using install.packages function.

Type

library(ggplot2)

library(mclust)


Press Ctrl + S keys to save the script.

At the top of the script, type the following commands.


Press Ctrl + S keys to save the script.

Highlight

library(ggplot2)

library(mclust)

Click the Run button.

Click the Run button to load these libraries.


[RStudio]

Highlight

Iris data set in the source window.


Click on the iris data set.


For the k-means clustering, we need to select two features of the data set.


So, we need to find the features that separate the data points clearly.


Hence, we will plot two graphs:

  • Sepal Length versus Sepal Width Width
  • Petal Length Length versus Petal Width
Point to Sepal Length versus Sepal Width.


[Rstudio]

Type

ggplot(iris,aes(x = Sepal.Length, y = Sepal.Width, col= Species)) + geom_point()


Click on Save button.

First, we will plot Sepal Length versus Sepal Width.

Type the following command.

Save the script.

Highlight

ggplot(iris,aes(x = Sepal.Length, y = Sepal.Width, col= Species)) + geom_point()


Click the Run button.

Select this command and run it to get a plot.
Drag Boundary I will drag the boundary to see the plot clearly.
Highlight

Output in Plot Window

As we can see, setosa is clearly distinguished.


But virginica and versicolor are not clearly separated.

Drag the boundary. Drag the boundary to see the Source window clearly.
Point to Petal Length versus Petal Width.


Type

ggplot(iris,aes(x = Petal.Length, y = Petal.Width, col= Species)) + geom_point()

Now we will plot Petal Length versus Petal Width.


Type the following command.

Highlight

ggplot(iris,aes(x = Petal.Length, y = Petal.Width, col= Species)) + geom_point()


Click on Save and Run buttons.

Save and run this command to see the plot.
Drag boundary to see the plot window clearly. Drag the boundary to see the plot window clearly.
Highlight Output in Plot Window Notice that Petal Length and Petal Width clearly separate the 3 species of iris.


Hence we should choose these two features.

Drag the boundary. Drag the boundary to see the Source window clearly.
Highlight Iris in source window


Highlight Species column

In this data set we have three species of iris.


In this case we already know that we need three clusters.


Hence, we will not be using the Elbow method.


To know more about it, please refer to the Additional Material on this tutorial page.

[Rstudio]

Type

km <- kmeans(iris[,3:4],3,nstart = 20)

Now let’s use the kmeans() function to perform k-means clustering.


Type the following command.

Highlight iris[,3:4]


Highlight nstart=20

Let me explain the parameters of this function.


This command uses Petal Length and Petal Width for k-means clustering.


This is used to find the best starting points of the centroids.

[Rstudio]

Click on Save and Run buttons.


Highlight km <- kmeans(iris[,3:4],3,nstart = 20)

Save the command and run it.


It runs 20 iterations and randomly initializes centroids.


Then it chooses the best configuration of centroids for clustering.

[RStudio]

Click the iris data set table.

We need to analyze our model to determine its performance.


Since the iris data set used here has labels, we will just tabulate the data.

[Rstudio]

Type

table(km$cluster,iris$Species)

Click on Save and Run buttons.

Type the following command.

Save the script and run the command.

Drag boundary to see the console window. Drag the boundary to see the Console window clearly.
[RStudio]

Highlight Row and Column in output

This table compares between predicted species and actual species.


Each row contains data points of 1 cluster.


Each column contains the species name.


Hence each cell gives the number of points belonging to a particular species.

[RStudio]

Highlight Output in the Console


Point to Setosa, versicolor and virginica.

50 Setosa samples have been clustered together.


2 Versicolor samples have been incorrectly clustered.


4 Virginica samples have been incorrectly clustered.


Overall, the model has misclassified only 6 samples.

Cursor in Rstudio window. Now we will calculate the accuracy of the model.


For this we will use the Adjusted Rand Index.

Show Slide

Adjusted Rand Index

  • This is a measure of similarity between two clusters.
  • It ranges from -1 to +1, where -1 is bad and +1 is good.
Let us switch to RStudio.
[RStudio]

Point to mclust library.


Type

adjustedRandIndex(km$cluster, iris$Species)


mclust library contains the function to calculate the adjusted RAND index.


Type the following command.

Click on Save and Run buttons. Save and run the command.
[RStudio]

Highlight Output on Console

The adjusted RAND index is very close to 1.


This means that our model has performed very well on this data set.

With this we come to the end of this tutorial. Let us summarize.
Show Slide

Summary

In this tutorial we have learnt:
  • Unsupervised Learning and its applications
  • k-means clustering on iris data set
  • Measure the performance using Adjusted RAND Index.
Here is an assignment for you.
Show Slide

Assignment

  • Using inbuilt PlantGrowth data set, perform k-means clustering.
  • Evaluate using adjusted RAND index.
Show Slide

About the Spoken Tutorial Project

The video at the following link summarises the Spoken Tutorial project.

Please download and watch it.

Show Slide

Spoken Tutorial Workshops

We conduct workshops using Spoken Tutorials and give certificates.


Please contact us.

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Spoken Tutorial Forum to answer questions


Do you have questions in THIS Spoken Tutorial?

Please visit this site.

Choose the minute and second where you have the question.

Explain your question briefly.

The FOSSEE project will ensure an answer.

You will have to register to ask questions.

Show Slide

Forum to answer questions

Do you have any general/technical questions?

Please visit the forum given in the link.

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Textbook Companion


The FOSSEE team coordinates the coding of solved examples of popular books and case study projects.


We give certificates to those who do this.


For more details, please visit these sites.

Show Slide

Acknowledgment

The Spoken Tutorial and FOSSEE projects are funded by the Ministry of Education Govt of India.
Show Slide

Thank You

This tutorial is contributed by Tanmay Srinath and Madhuri Ganapathi from IIT Bombay. Thank you for watching.

Contributors and Content Editors

Madhurig, Nancyvarkey

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