K-Means Clustering

 Introduction to Unsupervised Learning and Clustering

What is Unsupervised Learning?

Unsupervised learning is a type of machine learning where the model is trained using unlabeled data. Unlike supervised learning, there is no predefined output or target variable. The algorithm independently identifies hidden patterns, structures, or relationships within the dataset.

In unsupervised learning:

• There are no class labels
• The system learns patterns automatically
• It focuses on data exploration and structure discovery

The two main techniques in unsupervised learning are:

1.     Clustering

2.     Association Rule Learning

Among these, clustering is one of the most widely used and powerful methods.

 

Introduction to Clustering

Clustering is an unsupervised learning technique that groups similar data points together into clusters. The goal is to ensure:

·         Data points within the same cluster are more similar

·         Data points from different clusters are less similar

In simple terms:

Clustering means dividing data into meaningful groups based on similarity.

Clustering does not require labeled data, which makes it highly useful in real-world data analysis where labeled datasets are often unavailable.

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Why is Clustering Important?

Clustering helps in:

·         Discovering hidden patterns

·         Understanding data distribution

·         Segmenting large datasets

·         Supporting strategic decision-making

·         Preparing data for further predictive modeling

It plays a crucial role in research, healthcare, finance, marketing, and artificial intelligence.

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When is Clustering Used?

Clustering is applied in many domains:

1️. Customer Segmentation

Grouping customers based on:

·         Age

·         Income

·         Buying behavior

·         Preferences

2️.  Healthcare and Medical Research

·         Grouping patients by symptoms

·         Identifying disease risk categories

·         Age-group based disease analysis

·         Medical image grouping

3️. Image Segmentation

·         Dividing images into regions

·         Identifying objects

·         Medical image classification preprocessing

4️. Text and Document Analysis

·         Topic modeling

·         Document grouping

·         Sentiment-based segmentation

5.  Anomaly Detection

·         Detecting unusual patterns

·         Fraud detection

·         Cybersecurity threat identification

 

Types of Clustering Algorithms

Clustering algorithms are categorized based on how they form clusters.

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1️. Partition-Based Clustering

These algorithms divide data into a predefined number (K) of clusters.

🔹 K-Means Clustering

Problem Statement :

The objective of this study is to use K-Means clustering to segment universities based on academic performance indicators and institutional characteristics such as SAT scores, selectivity, student–faculty ratio, expenses, and graduation rate, in order to identify natural groupings and meaningful patterns within the dataset.

Dataset Description

The dataset contains academic and institutional performance indicators for Universities.

Download Dataset

Variables Explanation

Column Name	Description
Univ	Name of the University
SAT	Average SAT score of admitted students
Top10	Percentage of students from top 10% of high school class
Accept	Acceptance rate (%)
SFRatio	Student-to-Faculty ratio
Expenses	Annual academic expenses (USD)
GradRate	Graduation rate (%)

How It Works:

1.      Select number of clusters (K)

2.      Initialize centroids randomly

3.      Assign data points to nearest centroid

4.      Recalculate centroids

5.      Repeat until stable

Advantages:

·         Simple and fast

·         Efficient for large datasets

Limitations:

·         Requires predefined K

·         Sensitive to outliers

·         Works best for spherical clusters