View source for Machine-Learning-using-R - old 2022/C2/Unsupervised-Learning/English

'''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, spoken tutorial, video tutorial.



{| border=1
| align=center| '''Visual Cue'''
| align=center| '''Narration'''

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|| '''Show Slide'''

'''Opening Slide'''
|| Welcome to this spoken tutorial on '''Unsupervised Learning'''. 
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|| '''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. 
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|| '''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.

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|| '''Show Slide'''

'''Unsupervised Learning'''
|| Let us now learn about '''Unsupervised learning'''.
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|| '''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.

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|| '''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.

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|| '''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'''. 
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|| '''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'''.
|- 
|| Cursor in the '''UnsupervisedLearning''' folder.
|| 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.
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|| '''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?

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|| '''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 i'''ris[,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'''.

|- 

|| Only Narration
|| 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'''.

|- 

|| '''Show Slide'''

'''Assignment'''
||Here is an assignment for you.

* 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.

|- 

|| '''Show Slide'''

'''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.
|-

|| '''Show Slide'''

'''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.

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