K Means is one of the most popular Unsupervised Machine Learning Algorithms used for solving classification problems in data science, making it a crucial skill for those aspiring to excel in a data scientist role. K Means segregates unlabeled data into various groups, known as clusters, by identifying similar features and common patterns within the dataset. This tutorial aims to provide a comprehensive understanding of clustering, with a specific focus on the K Means clustering algorithm and its implementation in Python. By delving into the nuances of K means clustering in Python, you will gain valuable insights into how to effectively organize and analyze data. Additionally, the tutorial will guide you on determining the optimum number of clusters for a dataset, enhancing your ability to apply K means clustering in practical scenarios.

**Learning Objectives**

- Understand what the K-means clustering algorithm is.
- Develop a good understanding of the steps involved in implementing the K-Means algorithm and finding the optimal number of clusters.
- Implement K means Clustering in Python with scikit-learn library.

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

- Introduction
- What Is Clustering?
- What Is K-Means Clustering Algorithm?
- What is K-Means clustering method in Python?
- How K Means Clustering in Python Works?
- Diagrammatic Implementation of K-Means Clustering
- Choosing the Optimal Number of Clusters
- Python Code for K-Means Clustering:
- WCSS and Elbow Method
- Conclusion
- Frequently Asked Questions

Suppose we have N number of unlabeled multivariate datasets of various animals like dogs, cats, birds, etc. ** The technique of segregating these datasets into various groups on the basis of having similar features and characteristics is called clustering**.

The groups being formed are known as clusters. Clustering techniques are used in various fields, such as image recognition, spam filtering, etc. They are also used in unsupervised learning algorithms in machine learning, as* they can segregate multivariate data into various groups, without any supervisor, on the basis of common patterns hidden inside the datasets.*

The k-means clustering algorithm is an Iterative algorithm that divides a group of n datasets into k different clusters based on the similarity and their mean distance from the centroid of that particular subgroup/ formed.

K, here is the pre-defined number of clusters to be formed by the algorithm. If K=3, It means the number of clusters to be formed from the dataset is 3.

The implementation and working of the K-Means algorithm are explained in the steps below:

**Step 1:** Select the value of K to decide the number of clusters (n_clusters) to be formed.

**Step 2:** Select random K points that will act as cluster centroids (cluster_centers).

**Step 3:** Assign each data point, based on their distance from the randomly selected points (Centroid), to the nearest/closest centroid, which will form the predefined clusters.

**Step 4:** Place a new centroid of each cluster.

**Step 5:** Repeat step no.3, which reassigns each datapoint to the new closest centroid of each cluster.

**Step 6:** If any reassignment occurs, then go to step 4; else, go to step 7.

**Step 7:** Finish

K-Means clustering is a method in Python for grouping a set of data points into distinct clusters. The goal is to partition the data in such a way that points in the same cluster are more similar to each other than to points in other clusters. Here’s a breakdown of how to use K Means clustering in Python:

**Import Libraries:**

- First, you need to import the necessary libraries. In Python, the popular scikit-learn library provides an implementation of K-Means.

`from sklearn.cluster import KMeans`

**Prepare Your Data:**

- Organize your data into a format that the algorithm can understand. In many cases, you’ll have a 2D array or a pandas DataFrame.

```
import numpy as np
data = np.array([[1, 2], [5, 8], [1.5, 1.8], [8, 8], [1, 0.6], [9, 11]])
```

**Choose the Number of Clusters (K):**

- Decide on the number of clusters you want the algorithm to find. This is often based on your understanding of the data or through techniques like the elbow method.

```
kmeans = KMeans(n_clusters=2)
```

**Fit the Model:**

- Train the K-Means model on your data.

```
kmeans.fit(data)
```

**Get Results:**

- Once the model is trained, you can get information about the clusters.

```
# Get the cluster centers
centroids = kmeans.cluster_centers_
# Get the labels (cluster assignments for each data point)
labels = kmeans.labels_
```

In this example, n_clusters=2 indicates that we want the algorithm to find two clusters. The fit method trains the model, and then you can access information about the clusters, such as the cluster centers and labels. Visualizing the results can be helpful to see how well the algorithm grouped your data points.

Here is Step-by-Step Explanation that How K-means Clustering in Python works:

**Initialize Centroids**:

- Randomly choose K data points from the dataset to be the initial centroids. K is the number of clusters you want to create.

**Assign Data Points to Nearest Centroid:**

- For each data point in the dataset, calculate the distance to each centroid.
- Assign the data point to the cluster whose centroid is the closest (usually using Euclidean distance).

**Update Centroids:**

- Recalculate the centroids of the clusters by taking the mean of all the data points assigned to each cluster.

**Repeat:**

- Repeat steps 2 and 3 until convergence. Convergence occurs when the centroids no longer change significantly or after a predefined number of iterations.

**Final Result:**

- The algorithm converges, and each data point is assigned to one of the K clusters.

Here’s a simple example using Python with the popular machine learning library, scikit-learn:

```
from sklearn.cluster import KMeans
import numpy as np
# Sample data
data = np.array([[1, 2], [5, 8], [1.5, 1.8], [8, 8], [1, 0.6], [9, 11]])
# Specify the number of clusters (K)
kmeans = KMeans(n_clusters=2)
# Fit the data to the algorithm
kmeans.fit(data)
# Get the cluster centroids and labels
centroids = kmeans.cluster_centers_
labels = kmeans.labels_
print("Centroids:")
print(centroids)
print("Labels:")
print(labels)
```

**Step 1: **Letâ€™s choose the number k of clusters, i.e., K=2, to segregate the dataset and put them into different respective clusters. We will choose some random 2 points which will act as centroids to form the cluster.

**Step 2:** Now, we will assign each data point to a scatter plot based on its distance from the closest K-point or centroid. It will be done by drawing a median between both the centroids.

**Step 3: **points on the left side of the line are near the blue centroid, and points to the right of the line are close to the yellow centroid. The left forms a cluster with the blue centroid, and the right one with the yellow centroid.

**Step 4:** Repeat the process by choosing **a new centroid**. To choose the new centroids, we will find the new center of gravity of these centroids, as depicted below.

**Step 5: **Next, we will reassign each data point to the new centroid. We will repeat the same process as above (using a median line). The yellow data point on the blue side of the median line will be included in the blue cluster.

**Step 6: **As reassignment has occurred, we will repeat the above step of finding new k centroids.

**Step 7: **We will repeat the above process of finding the center of gravity of k centroids, as depicted below.

**Step 8: **After finding the new k centroids, we will again draw the median line and reassign the data points, like the above steps.

**Step 9:** We will finally segregate points based on the median line, such that two groups are being formed and no dissimilar point is to be included in a single group.

The final cluster formed is like this:

*The number of clusters that we choose for the algorithm shouldnâ€™t be random. Each and every cluster is formed by calculating and comparing the mean distances of each data point within a cluster from its centroid.*

We can choose the right number of clusters with the help of the Within-Cluster-Sum-of-Squares (WCSS) method. WCSS stands for the sum of the squares of distances of the data points in each and every cluster from its centroid.

The main idea is to minimize the distance (e.g., euclidean distance) between the data points and the centroid of the clusters. The process is iterated until we reach a minimum value for the sum of distances.

Here are the steps to follow in order to find the optimal number of clusters using the elbow method:

**Step 1:** Execute the K-means clustering on a given dataset for different K values (ranging from 1-10).

**Step 2: **For each value of K, calculate the WCSS value.

**Step 3: **Plot a graph/curve between WCSS values and the respective number of clusters K.

**Step 4: **The sharp point of bend or a point (looking like an elbow joint) of the plot, like an arm, will be considered as the best/optimal value of K.

**Python Implementation:**

Importing relevant libraries

```
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from sklearn.cluster import KMeans
```

Loading the data

```
data = pd.read_csv('Countryclusters.csv')
data
```

Plotting the data

Selecting the feature

```
x = data.iloc[:,1:3] # 1t for rows and second for columns
x
```

Clustering

```
kmeans = KMeans(3)
means.fit(x)
```

Clustering results

```
identified_clusters = kmeans.fit_predict(x)
identified_clusters
```

`array([1, 1, 0, 0, 0, 2])`

```
data_with_clusters = data.copy()
data_with_clusters['Clusters'] = identified_clusters
plt.scatter(data_with_clusters['Longitude'],data_with_clusters['Latitude'],c=data_with_clusters['Clusters'],cmap='rainbow')
```

```
wcss=[]
for i in range(1,7):
kmeans = KMeans(i)
kmeans.fit(x)
wcss_iter = kmeans.inertia_
wcss.append(wcss_iter)
number_clusters = range(1,7)
plt.plot(number_clusters,wcss)
plt.title('The Elbow title')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
```

**This method shows that 3 is a good number of clusters.**

To summarize everything that has been stated so far, k means clustering in python is a widely used unsupervised machine learning technique that enables the grouping of data into clusters based on similarity. It is a simple algorithm that can be applied to various domains and data types, including image and text data. k-means can be used for a variety of purposes. We can use it to perform dimensionality reduction also, where each transformed feature is the distance of the point from a cluster center.

**Key Takeaways**

- K-means is a widely used unsupervised machine learning algorithm for clustering data into groups (also known as clusters) of similar objects.
- The objective is to minimize the sum of squared distances between the objects and their respective cluster centroids.
- The k-means clustering algorithm is limited as it can not handle complex and non-linear data.

A. n_init is an integer and represents the number of times or the number of iterations the k-means algorithm will be run independently.

A. Advantages of K-means Clustering include its simplicity, scalability, and versatility, as it can be applied to a wide range of data types. Disadvantages include its sensitivity to the initial placement of centroids and its limitations in handling complex, non-linear data. k-means is also sensitive to outliers.

A. In K-Means, random_state represents random number generation for centroid initialization. We can use an Integer value to make the randomness fixed or constant. Also, it helps when we want to produce the same clusters every time.

*The media shown in this article are not owned by Analytics Vidhya and is used at the Authorâ€™s discretion.*

Lorem ipsum dolor sit amet, consectetur adipiscing elit,

Become a full stack data scientist
##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

##

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 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

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

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

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

In part 2 - "What is K Means Algorithm" - you forgot a very important step, which is to determine the new cluster center by computing the average of the assigned points

hi it is a very good article. Please can you also write an article with coding how to choose cluster head based on battery power in each cluster?

thanks for your helpful article I just wanted to say there are some parts that needs to be corrected : first one : 5. Python Implementation >> Clustering >> means -> kmeans second one : in section "Trying different method ( to find no .of clusters to be selected) WCSS and Elbow Method" after "for" loop indent is forgotten