Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Clustering Machine Learning Algorithm using K Means

Amrutha
Amrutha Last Updated : 11 Apr, 2023
6 min read

This article was published as a part of the Data Science Blogathon.

Table of Contents

1. Introduction

2. Clustering

3. Types of Clustering

4. K-Means Clustering

5. Finding K value

6. Elbow Method

7. Silhouette Method

8. Implementation

9. Conclusion

Introduction

In this article, we will learn about K-Means clustering in detail. K-Means is one of the most popular and simplest machine learning algorithms. K-Means is used when we have unlabeled data. So now we will learn everything in this article. Before going into that first we will learn what is clustering.

Clustering

Clustering: The process of dividing datasets into groups consisting of similar data points is called clustering.

Clustering is an unsupervised learning technique.

Imagine a supermarket where all the items were arranged. All the vegetables were placed in the vegetable section, fruits in the fruits section, and all. So that you can easily shop and get things that you want. They were not mixed up. This is an example of clustering. They were divided into clusters based on their similarity.

Let’s see some examples of clustering.

Whenever you visit Amazon, you will see a recommendation list of some products. This is based on your past purchases. This is done because of clustering.

And even same with Netflix. You will get movie recommendations based on your watch history.

Types of Clustering

1. Exclusive Clustering

2. Overlapping Clustering

3. Hierarchical Clustering

Exclusive Clustering: Exclusive Clustering is the hard clustering in which data point exclusively belongs to one cluster.

For example, K-Means Clustering.

Clustering Machine Learning Algorithm
Source: Author

Here you can see all similar datapoints are clustered. All the blue-colored data points are clustered into the blue cluster and all the red-colored data points are clustered into the red cluster.

Overlapping Clustering: Overlapping clustering is the soft cluster in which data point belongs to multiple clusters.

For example, C-Means Clustering.

Clustering Machine Learning Algorithm
Source-https://miro.medium.com/max/932/1*UF3wS7TC30NqEIrFS72sVw.png

In this, we can see that some of the blue data points and some of the pink data points are overlapped.

Hierarchical Clustering: Hierarchical clustering is grouping similar objects into groups. This forms the set of clusters in which each cluster is distinct from another cluster and the objects within that each cluster is similar to each other.

I know this might be a little confusing. Let’s understand it in detail.

Hierarchal Clustering

Source-Author

Observe this picture. There are Six different data points namely, A, B, C, D, E, and F.

Coming to case1, A and B are clustered based on some similarities whereas E and D are clustered based on some similarities.

Coming to case2, the combination of A and B is similar to C so the combination of A and B is grouped with C.

Coming to case3, the combination of D and E is similar to F. So the combination of D and E is grouped with F.

Coming to the last case, the combination of A, B, C and combination of D, E, F are quite similar so all these points are grouped into a single cluster.

This is how hierarchical clustering works.

Let’s learn about K-Means Clustering in detail.

K-Means Clustering

K-Means Clustering: The algorithm which groups all the similar data points into a cluster is known as K-Means Clustering. This is an unsupervised machine learning algorithm. This contains no labeled data. K-Means is a centroid-based algorithm in which each group has a centroid.

Here K in K-Means is the number of clusters. In this K-Means algorithm first, we randomly assign some centroids to the dataset. And then clusters are formed by assigning data points to the cluster to which the data point is near to the corresponding cluster. From those clusters, new centroids were formed with the mean data points. This process will continue until the model is optimized which means the final centroids will not change even for the next iteration.

Let us understand it in detail by taking some random data points. For these points, two clusters were formed with random centroids.

This is the first iteration. Centroids were completely random and clusters will look like this.

K Means Clustering

Source: Author

Now coming to the second iteration, the mean of data points were taken and new centroids were formed. Now the clusters will look like this.

Clustering Machine Learning Algorithm
Source: Author

Now coming to the third iteration, again centroids were reassigned based on mean data points. Clusters will be like this.

Clustering Machine Learning Algorithm
Source: Author

Here the model is optimized. Centroids were not changing though we iterate again. So final clusters were formed.

Finding K value

In this article, we will see 2 methods to find the K value for K-Means.

1. Elbow Method

2. Silhouette Method

Elbow Method

Elbow Method: This is one of the most popular methods that is used to find K for K-Means.

For this, we have to learn something known as WSS(Within the sum of squares).

WSS: The WSS is defined as the sum of squared distance between each member of the cluster and its centroid.

Elbow Method
Source: Author

Where

p(i)=data point

q(i)=closest centroid to the data point

Here in the elbow method, the K value is chosen after the decrease of WSS is almost constant.

How to find the Optimal Number of Clusters in K-means? Elbow and Silhouette Methods - Machine Learning Interviews

Source-https://machinelearninginterview.com/wp-content/uploads/2020/12/elbow.png

In the above picture, you can see the elbow point, which is 3. After that point, WSS is almost constant. So 3 is selected as K.In this way elbow method is used for finding the value of K

Silhouette Method

Silhouette Method: Here in the silhouette method, we will compute the silhouette score for every point.

Silhouette Coefficient for the point= (b-a)/max(a,b)

Where

a=mean intra-cluster distance

b=mean nearest cluster distance

Silhouette coefficient for dataset = Mean Silhouette Coefficient over points.

If we draw a graph for these points, we will get something like this.

Silhouette Coefficient | Clustering Machine Learning Algorithm
Source-http://i.stack.imgur.com/iAWnF.png

So here we can see the highest silhouette coefficient is for K = 3. In this way, the silhouette method is used for finding K.

But in the Silhouette method, there are some chances of getting overfitted to some extent. Because by increasing the number of clusters, the size of clusters becomes small and distance between other clusters may decrease and finally leads to overfitting sometimes. But in a lot of cases, it works well.

It is available in scikit learn library.

sklearn.metrics.silhouette_score

Implementation

Create a model by importing KMeans which is an inbuilt model in scikit learn library. So we can directly import it. Using the fit method train the model with data. and finally, use the predict method to predict the outcome of desired data.

And finally, in visualizations, you can see all the data points were clustered into different groups, and centroids were also assigned.

This is how K-Means Clustering is implemented.

In this colab notebook, you can see how K Means is implemented by taking some dataset.

Conclusion

In this article, we have learned about the K Means clustering algorithm from scratch to implementation. First, we have seen clustering and then finding K value in K Means. For that, we have seen two different methods. Overall we have seen the importance of the K Means clustering algorithm. Finally, we had implemented the code for this algorithm in the Jupiter notebook.

 Hope you guys found it useful. Read more articles on machine learning algorithms on AV blog.

Connect with me on Linkedin:

https://www.linkedin.com/in/amrutha-k-6335231a6vl/

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion.

Amrutha
Amrutha

This is Amrutha, I am pursuing B.Tech in the Computer science Department. I am interested in developing ML Models with python and Data Analysis. And also I have an interest in Web Development. I hope my articles in Analytics Vidhya help you to learn better. Thank you!!

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details