• Markings will be based on the correctness and soundness of the outputs.
• Marks will be deducted in case of plagiarism.
• Proper indentation and appropriate comments (if necessary) are mandatory.
• Use of frameworks like scikit-learn etc is allowed.
• All benchmarks(accuracy etc), answers to questions and supporting examples should be added in a separate file with the name ‘report’.
• All code needs to be submitted in ‘.py’ format. Even if you code it in ‘.ipynb’ format, download it in ‘.py’ format and then submit
• You should zip all the required files and name the zip file as:
◦ <roll_no>_assignment_<#>.zip, eg. 1501cs11_assignment_01.zip.
• Upload your assignment ( the zip file ) in the following link:
◦ https://www.dropbox.com/request/Y7uWRDGgJj2uKkiOYDGi
Problem Statement:
• The assignment targets to implement DBScan algorithms to cluster the 3 datasets with blob, moon and circle structures. Each csv file consists of two columns in the datasets.
◦ https://www.dropbox.com/scl/fo/9jbpw1ah58jcvva5bzwdl/h?dl=0&rlkey=4a
o0wdd9ipyvwuhpp0edvqic2
Implementation Steps:
• Design and implement DBSCAN algorithm to cluster the 3 datasets mentioned. Assume hyperparameters wherever necessary.
• Use Silhouette index to evaluate the clustering quality.
• Run dataset for the K-means algorithm implemented in assignment 1. Set the num_clusters for K-means to the no. of clusters from DBSCAN. Compare it to DBSCAN in terms of cluster visualization and Silhouette index score.
Documents to submit:
• Model code
• Silhouette index
• Visualization of clusters for DBSCAN and K-means clustering
