Using K-means Cluster Analysis to Discover Hidden Patterns

 

Introduction

We would like to take this opportunity to welcome you to this article on our blog, in which we will dig into the fascinating realm of k-means cluster analysis, a well-known unsupervised machine learning method. The results of a cluster analysis may be used to divide the total number of observations in a dataset into a number of distinct but related subsets, or clusters, according to the degree to which they share characteristics across many dimensions. We are able to find subgroups of observations that display similar patterns of response on a set of clustering variables by doing a k-means cluster analysis. This allows us to make more informed decisions. Quantitative measures make up the vast majority of these variables; however, binary variables may also be included.

 

Acquiring Knowledge of the K-means Clustering Method

K-means cluster analysis is a kind of unsupervised learning that seeks to divide observations into separate clusters, with the expectation that each observation will belong to exactly one cluster. The analysis begins by randomly allocating each observation to one of the clusters. Next, the clusters are repeatedly optimized to minimize the within-cluster sum of squares, and finally, the analysis is complete. The number of clusters whose location we want to determine is denoted by the letter "k" in the term "k-means."

 

The Steps Involved

 

Bringing in the Necessary Library Packages

Importing the required libraries into our Python environment is the first step in getting started. For the purpose of carrying out k-means cluster analysis, we will be using scikit-learn, which is a well-known machine learning toolkit. This library gives us access to the KMeans class.

 

The Dataset Is Being Loaded

Next, we need to load our dataset, which includes the clustering variables and observations that will serve as the foundation for our study.

 

K-means Cluster Analysis Being Carried Out

 

 

We will make use of the KMeans class in order to determine the subgroups of observations that exhibit response patterns that are comparable to one another. After that, we fit the model to our data while specifying the number of clusters that we want to build (k).

 

Extracting the Assignments to the Clusters

After we have finished the k-means cluster analysis, we will be able to get the cluster assignments that correspond to each observation. Each observation is given a label that specifies which cluster it falls under.

 

The process of analyzing and interpreting the results is as follows

It is now time to examine and make sense of what the findings mean. You may obtain insight into the many subgroups that have been discovered by the study by examining the features of each cluster. For example, you might look at the mean values of the clustering variables. In addition, you may improve your comprehension by visualizing the clusters by selecting the relevant plots or graphs and putting them together.

 

Python Code

 

# Import the required libraries

from sklearn.cluster import KMeans

import numpy as np

 

# Load the dataset

X = np.array([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]])

 

# Create an instance of the KMeans class

kmeans = KMeans(n_clusters=2, random_state=0)

 

# Fit the model to the data

kmeans.fit(X)

 

# Extract the cluster assignments

cluster_assignments = kmeans.labels_

 

# Print the cluster assignments

print("Cluster assignments:", cluster_assignments)

 

Summary

 

k-means cluster analysis is an unsupervised approach of machine learning that is used to find subgroups of observations that have similar response patterns. In this blog article, we discussed the notion of k-means cluster analysis. We were able to divide the dataset into a number of separate groups by using the k-means cluster analysis. These groupings were determined by the clustering variables. Keep in mind that you are not forced to execute the cluster analysis on a test dataset unless you desire to do so. You have the option to do so. You may skip the step of separating your dataset into training and test sets if the number of observations in your dataset is not very large. However, you must ensure that your written summary include an explanation of the reasoning behind this choice.