K-Means Clustering

Introduction

K-Means clustering is a popular unsupervised machine learning algorithm used for partitioning data into clusters based on similarity. It aims to group data points into k clusters, where each cluster represents a group of similar data points. K-Means clustering is widely used in various fields such as image segmentation, customer segmentation, and anomaly detection.

How K-Means Clustering Works

K-Means clustering works by iteratively assigning data points to the nearest cluster centroid and then updating the centroids based on the mean of the data points assigned to each cluster. This process continues until convergence, where the cluster assignments and centroids no longer change significantly.

Detailed Steps:

1. Initialization:

   • Randomly select k data points from the dataset as the initial cluster centroids.
   • Alternatively, use k-means++ initialization for more robust initialization.

2. Assignment (Expectation):

   • Assign each data point to the nearest cluster centroid based on a distance metric (typically Euclidean distance).
   • Each data point is assigned to the cluster with the nearest centroid.

3. Update Centroids (Maximization):

   • Calculate the mean of the data points assigned to each cluster.
   • Update the cluster centroids to the computed means.

4. Repeat:

   • Repeat steps 2 and 3 until convergence, where the cluster assignments and centroids no longer change significantly.
   • Convergence is typically determined by a predefined tolerance or a maximum number of iterations.

Key Parameters in K-Means Clustering

• K: The number of clusters to create. Choosing an appropriate value of k is crucial and can significantly impact the clustering results.
• Initialization Method: The method used to initialize the cluster centroids, such as random initialization or k-means++ initialization.
• Distance Metric: The metric used to compute the distance between data points, such as Euclidean distance, Manhattan distance, or cosine similarity.

Advantages of K-Means Clustering

• Simple and easy to implement.
• Scalable to large datasets.
• Efficient in terms of computational complexity.
• Can handle clusters of different shapes and sizes.

Limitations of K-Means Clustering

• Requires the number of clusters (k) to be specified in advance.
• Sensitive to the initial cluster centroids, which can lead to suboptimal solutions.
• Assumes clusters are spherical and of similar size, which may not always be the case.
• May converge to local optima, depending on the initialization.

Applications of K-Means Clustering

• Customer segmentation in marketing.
• Image compression and segmentation.
• Anomaly detection in cybersecurity.
• Document clustering in natural language processing.
• Recommendation systems in e-commerce.

Datasets

This repository includes sample datasets in CSV format that can be used to practice K-Means clustering.

Repository Structure

└── K-Means_Clustering/
    ├── Mall_Customer_Kmeans.ipynb
    ├── Mall_Customers.csv
    ├── Mall_Customers_Report.html
    ├── README.md
    └── requirements.txt

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Getting Started

Requirements

Ensure you have the following dependencies installed on your system:

• JupyterNotebook

Installation

1. Clone the K-Means Clustering repository:

git clone https://github.com/sumony2j/K-Means_Clustering.git

2. Change to the project directory:

cd K-Means_Clustering

3. Install the dependencies:

pip install -r requirements.txt

Running K-Means Clustering

Use the following command to run K-Means Clustering:

jupyter nbconvert --execute notebook.ipynb

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Contributing

Contributions are welcome! Here are several ways you can contribute:

• Submit Pull Requests: Review open PRs, and submit your own PRs.
• Join the Discussions: Share your insights, provide feedback, or ask questions.
• Report Issues: Submit bugs found or log feature requests for K-means_clustering.

Contributing Guidelines

1. Fork the Repository: Start by forking the project repository to your GitHub account.
2. Clone Locally: Clone the forked repository to your local machine using a Git client.

   git clone https://github.com/sumony2j/K-Means_Clustering.git

3. Create a New Branch: Always work on a new branch, giving it a descriptive name.

   git checkout -b new-feature-x

4. Make Your Changes: Develop and test your changes locally.
5. Commit Your Changes: Commit with a clear message describing your updates.

   git commit -m 'Implemented new feature x.'

6. Push to GitHub: Push the changes to your forked repository.

   git push origin new-feature-x

7. Submit a Pull Request: Create a PR against the original project repository. Clearly describe the changes and their motivations.

Once your PR is reviewed and approved, it will be merged into the main branch.