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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

Understanding KMeans Clustering for Data Science Beginners

Harika
Harika Last Updated : 12 Nov, 2024
5 min read
This article was published as a part of the Data Science Blogathon
image | Kmeans clustering

Introduction:

Clustering is an unsupervised learning method whose job is to separate the population or data points into several groups, such that data points in a group are more similar to each other dissimilar to the data points of other groups. It is nothing but a collection of objects based on similarity and dissimilarity between them.

KMeans clustering is an Unsupervised Machine Learning algorithm that does the clustering task. In this method, the ‘n’ observations are grouped into ‘K’ clusters based on the distance. The algorithm tries to minimize the within-cluster variance(so that similar observations fall in the same cluster).

KMeans clustering requires all variables to be continuous as it uses the distance measure and prior specification of the number of clusters(K).

In this blog, we will learn:

  • How does the KMeans algorithm work?
  • The Distance Metrics
  • How do we select the right K value?
  • Properties of clusters
  • Implementation of KMeans clustering in Python using sklearn library

How does the algorithm work?

In Hierarchical clustering methods, it is not required to specify K, but for KMeans we need to upfront specify the K.

  1. For a given value of K, initialize K number of centroids randomly and the data points are partitioned into K clusters
  2. compute the distance between each of the input to the K centroids and re-assign it to the cluster with the least distance
  3. after the re-assignment, update the centroid of each cluster by calculating the mean of the data points in the cluster
  4. repeat steps 2–3 until there is no re-assignment required
Kmeans clustering
Visual representation of working of Algorithm(Source)

Distance Metrics

KMeans is a centroid/distance-based algorithm where the distance between each point is calculated and then assigns it to a cluster. The distance can be calculated using

  • Euclidean Distance
  • City block/Manhattan Distance
distance measures
The formula for Euclidean and Manhattan distance measures

Properties of clusters

  • All the data points in a cluster should be similar to each other(homogeneity). Within Cluster Sum of Squares(WCSS) is the total sum of the squared average distance of all the points within a cluster to its centroid. The lesser the better.
  • Data points from different clusters should be heterogeneous. Between Clusters Sum of Squares (BCSS) is the total sum of the squared average distance between all centroids. A larger value indicates that clusters are spread out, while a smaller value indicates clusters are close to each other.

How do we select the right K value?

If we have two-dimensional data, we can visualize the data using a scatter plot and decide the number of clusters. But if we have multidimensional data, it is indeed difficult to visualize. Wrong k value might result in wrong or unstable clusters.

So we use a technique called Elbow Method to obtain an optimal K value.

In this method, we plot the WCSS values for a range of K.

Suppose, K=3, we will have three clusters whose Within Cluster Sum of Squares are WCSS1, WCSS2, WCSS3 respectively. Total WCSS is the sum of WCSS1, WCSS2, WCSS3.

WCSS = WCSS1 + WCSS2 + WCSS3

After calculating the WCSS plot it against different values of K. Look for an elbow shape in the graph to find the optimum K value.

elbow method | Kmeans clustering
Elbow Curve Illustration

We can see a bend at K=4 in the above graph indicating 4 is the optimal number of clusters.

Implementation of KMeans clustering in Python using sklearn library

I will be taking the crime_data.csv dataset for our implementation.

About our dataset: It has five features — State, Murder, Assault, UrbanPop, Rape.

  • Murder — Murder rates in different states of United States
  • Assault — Assualt rate in different States of United States
  • UrbanPop — urban population in different States of United States
  • Rape — Rape rate in different States of United States

Let’s begin with importing necessary libraries

import pandas as pd
import NumPy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler

Create pandas data frame from crime_data.csv

data = pd.read_csv("crime_data.csv")
data.head()
data | Kmeans clustering

 

Check if there are any null values

data.isnull().any()
finding null values

 

Dropping the categorical feature and copy the remaining data to another data frame

mydata = data.iloc[:, data.columns!=’Unnamed: 0']
mydata.head()
top rows

 

Scaling Data (used MinMaxScaler)

scaler = MinMaxScaler()
norm_mydata = mydata.copy()
def minmaxscaler(x):
    for columnName, columnData in x.iteritems():
        x[columnName] = scaler.fit_transform(np.array(columnData).reshape(-1, 1))
    
minmaxscaler(norm_mydata)
norm_mydata.head()
normalized data

Scree plot or Elbow plot to find K

 

k = list(range(2,11))
sum_of_squared_distances = []
for i in k:
    kmeans = KMeans(n_clusters=i)
    kmeans.fit(norm_mydata)
    sum_of_squared_distances.append(kmeans.inertia_)

plt.figure(figsize=(10, 5))
plt.plot(k, sum_of_squared_distances, 'go--')
plt.xlabel('Number of Clusters')
plt.ylabel('Within Cluster Sum of squares')
plt.title('Elbow Curve to find optimum K')
elbow curve

Elbow Curve (Image by Author)

From the above figure, we find K=4 as the optimal value.

Building KMeans model with K=4 (Training and Predicting)

# Instantiating
kmeans4 = KMeans(n_clusters = 4)

# Training the model
kmeans4.fit(norm_mydata)

# predicting
y_pred = kmeans4.fit_predict(norm_mydata)
print(y_pred)

# Storing the y_pred values in a new column
data['Cluster'] = y_pred+1 #to start the cluster number from 1

Storing the centroids to a data frame

centroids = kmeans4.cluster_centers_
centroids = pd.DataFrame(centroids, columns=['Murder', 'Assault', 'UrbanPop', 'Rape'])
centroids.index = np.arange(1, len(centroids)+1) # Start the index from 1
centroids
finding centroides

 

Sample visualization of clusters

Let’s just take any two of the features and plot to see how the observations are clustered.

import seaborn as sns
plt.figure(figsize=(12,6))
sns.set_palette("pastel")
sns.scatterplot(x=data['Murder'], y = data['Assault'], hue=data['Cluster'], palette='bright')
visualization of clusters | Kmeans clustering
Visualization of Cluster

End Notes

In this blog, we’ve learned the working of the KMeans algorithm, different distance metrics used, and the Elbow curve method for finding the right K value. I hope you enjoyed reading this article, feel free to share it with your study buddies.

Check out my previous articles on KModes Clustering.

References

sklearn.cluster.KMeans

Elbow Method to find the K value

Scaling using sklearn MinMaxScaler

Other Blog Posts by me

Feel free to read my other blog posts from my Analytics Vidhya Profile.

You can find me on LinkedIn, Twitter just in case you would want to connect. I would be glad to connect with you.

For immediate exchange of thoughts, please write to me at [email protected].

Happy Learning!

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

blogathonclusteringkmeansmachine learning
Harika
Harika

Hi, my name is Harika. I am a Data Engineer and I thrive on creating innovative solutions and improving user experiences. My passion lies in leveraging data to drive innovation and create meaningful impact.

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Clustering Machine Learning Algorithm using K Means
Clustering Machine Learning Algorithm using K Means
A Simple Guide to Centroid Based Clustering (with Python code)
A Simple Guide to Centroid Based Clustering (with Python code)

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