akashkthkr / SWM573_G18_P7_Document_Clustering_Summarization_and_Visualization

Explore different clustering algorithms, Implemented on the dataset

SWM573 - Document Clustering Summarization and Visualization

Explore different clustering algorithms, Implemented on the dataset

Project Abstract & Aim

The aim of this project is to:

• Explore different clustering algorithms, Implemented on the dataset
• Cluster the news articles
• Recommend similar articles that are available similar to the documents
• Extract keywords in the articles and provide a short summary
• Apply Visualization techniques on the textual document to showcase relevancy
• Identifying anomalies in the dataset
• Identifying popular words in each group - like a word cloud

DataSet Used

Dataset used - 20 Newsgroups Dataset (http://qwone.com/~jason/20Newsgroups/) Using sklearn.datasets import fetch_20newsgroups

Run time and Instances

We used the google colab and Pycharm Jupyter notebook for additional CPU and GPU support and dataset storage

Algorithms and Visualization Techniques used

Clustering

• LDA
• HDBScan
• Agglomerative clustering

Visualization Techniques

• t-SNE
• UMAP
• Compression-VAE

Team Members

• Sagar Sharma (1219478715)
• Mounika Chadalavada (1222313482)
• Yasir Affan (1223348503)
• Akash Kant (1222368576)
• Swaapnika Motapothula (1219420111)
• Harshitha Sadula (1223715727)