CS Technical Workshop "Clustering" By: Ali Abbasi Tadi

Monday, October 16, 2023 - 14:00

The School of Computer Science is pleased to present...


Technical Workshop Series



Presenter:  – PhD Candidate Ali Abbasi Tadi

Date/Time: Monday, October 16th , 2:00 pm – 3:00 pm 

Location: 4th Floor (Workshop space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)

LATECOMERS WILL NOT BE ADMITTED once the presentation has begun.


Clustering is a way of grouping data points into different clusters consisting of similar data points. The objects with possible similarities remain in a group that has less or no similarities with another group.  In this workshop, we explore various clustering approaches in centralized environments and provide various metrics for finding the performance of clustering methods in the literature. We also provide basic concepts on some well-known clustering approaches and their differences, followed by the performance discussion of each clustering method. We introduce the ways by which we can improve clustering quality and justify the outperformance of the methods that we discuss.

Workshop Outline:

Clustering concepts and well-known approaches (k-means, Hierarchical, …). Quality metrics (Silhouette coefficient Rand Index, and Adjusted Rand Index). Basic Implementations of Clustering Approaches in Python


Basic Statistics concepts, basic Python programming


 Ali is pursuing his Ph.D. in computer science at the University of Windsor. His main research interest is security/privacy in machine learning. He has publications on private clustering in top conferences and peer-reviewed journals. He has received various scholarships from the University of Windsor and got 5th place in the iDash 2022 competition.  He has been invited as a speaker at the Advanced Computing Hub at the University of Windsor. He is serving as a program committee for some conferences and as a reviewer for peer-reviewed journals.  He is currently developing various ways for secure computation of transcriptomics data