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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
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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
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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
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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
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8 Ways to Improve Accuracy of Machine Learning Models
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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
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Simple Explanation To Understand K Means Clustering

Manoj
Manoj Last Updated : 01 Mar, 2021
4 min read

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

Overview

K-means clustering is a very popular and powerful unsupervised machine learning technique where we cluster data points based on similarity or closeness between the data points how exactly We cluster them? which methods do we use in K Means to cluster? for all these questions we are going to get answers in this article, before we begin take a close look at the below clustering example, what do you think? it’s easily interpretable, right? We clustered data points into 3 clusters based on their similarity or closeness.

K Means Clustering image
image source

Table Of Contents

1.introduction to K Means

2.K Means ++ Algorithm

3.How To Choose K Value in K Means?

4.Practical Considerations in K Means

5.Cluster Tendency

1. Introduction

Let’s simply understand K-means clustering with daily life examples. we know these days everybody loves to watch web series or movies on amazon prime, Netflix. have you ever observed one thing whenever you open Netflix? that is grouping movies together based on their genre i.e crime, suspense..etc, hope you observed or already know this. so Netflix genre grouping is one easy example to understand clustering. let’s understand more about k means clustering algorithm.

Definition: It groups the data points based on their similarity or closeness to each other, in simple terms, the algorithm needs to find the data points whose values are similar to each other and therefore these points would then belong to the same cluster.

so how does the algorithm find out values between two points to cluster them, the algorithm finds values is by using the method of ‘Distance Measure’. here distance measure is ‘Euclidean Distance’

K Means Clustering distance

The observations which are closer or similar to each other would have low Euclidean distance and then clustered together.

one more formula that you need to know to understand K means is ‘Centroid’. The k-means algorithm uses the concept of centroid to create ‘k clusters.’

K Means Clustering centroid

So now you are ready to understand steps in the k-Means Clustering algorithm.

 

Steps in K-Means:

step1:choose k value for ex: k=2

step2:initialize centroids randomly

step3:calculate Euclidean distance from centroids to each data point and form clusters that are close to centroids

step4: find the centroid of each cluster and update centroids

step:5 repeat step3

Each time clusters are made centroids are updated, the updated centroid is the center of all points which fall in the cluster. This process continues till the centroid no longer changes i.e solution converges.

You can play around with the K-means algorithm using the below link, try it.

https://stanford.edu/class/engr108/visualizations/kmeans/kmeans.html

So what next? how do you choose initial centroids randomly?

Here comes the concept of the k-Means++ algorithm.

2. K-Means ++ Algorithm:

I’m not going to stress you more on this so don’t worry. it is very easy to understand. So what is k-means++??? Let’s say we want to choose two centroids initially(k=2), you can choose one centroid randomly or you can choose one of the data points randomly. simple right? Our next task is to choose another centroid, how do you choose? any idea?
We choose the next centroid from the data points which is at a long distance from the existing centroid or the one which is at a long distance from an existing cluster that has a high chance of picking up.

 

3.How To Choose K Value In K-Means:

1.Elbow method

steps:

step1: compute clustering algorithm for different values of k.

for example k=[1,2,3,4,5,6,7,8,9,10]

step2: for each k calculate the within-cluster sum of squares(WCSS).

step3: plot curve of WCSS according to the number of clusters.

step4: The location of bend in the plot is generally considered an indicator of the approximate number of clusters.

 

wcss formula
elbow curve

 

4.Practical Considerations In K-Means:

  • A choosing number of Clusters in Advance(K).
  • Standardization of Data(scaling).
  • Categorical Data(can be solved with K-Mode).
  • Impact of initial Centroids and Outliers.

5. Cluster Tendency:

Before we apply a clustering algorithm to the given data, it is important to check whether the given data has some meaningful clusters or not. The process to evaluate the data to check if the data is feasible for clustering or not is known as ‘Clustering Tendency’.so we should not blindly apply the clustering method and we should check clustering tendency. how?
We use ‘Hopkins Statistic’ to know whether to perform clustering or not for a given dataset.it examines whether the data points differ significantly from uniformly distributed data in multidimensional space.
This concludes our article on the k-means clustering algorithm. In my next article, I will talk about the python implementation of the K-means clustering algorithm.
Thank you!

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

blogathonK-means Clustering
Manoj
Manoj

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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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