embedding-comparisons-in-clustering-application

• This repository presents a novel autoencoder architecture designed for news article clustering.
• It compares its performance against established methods like TF-IDF, GLoVe, Word2Vec, and BERT.
• Evaluation metrics include Davies-Bouldin and Calinski-Harabasz Indices.

Notebooks

Comparison of Existing Models

• Comparison_Existing_Models.ipynb: Compares existing models such as TF-IDF, GLoVe, Word2Vec, and BERT for news article clustering.

Autoencoder for News Clustering

• Autoencoder_Clustering.ipynb: Demonstrates an autoencoder-based approach for news article clustering, showcasing its superiority over traditional methods.

Getting Started

Cloning the Repository

1. Clone the repository:

   git clone https://github.com/KanishkRath/embedding-comparisons-in-clustering-application.git
   cd embedding-comparisons-in-clustering-application

2. Access the notebooks:

   • Locate the notebooks within the cloned repository to explore and execute them using Jupyter Notebook.

Usage

• Ensure you have Jupyter Notebook installed.
• Open the notebooks to run and explore the functionalities.
• Customize code segments or parameters for experimenting with different datasets or settings.

Comparison Summary

Model Comparison Table

Method	Davies Bouldin Index	Calinski Harabasz Index
TF-IDF	8.5827	14.52
Word2Vec	1.5521	484.77
GLoVe	1.7722	369.76
BERT	3.0890	125.49
Autoencoder	0.8967	1250.82
Autoencoder with One Hot Encoding	0.8288	3298.19