Clustering: Basic Approaches and Their Evaluation
When: Wednesday, February 24, 2020
Where: PGH 232
Time: 11:00 AM
Speaker: Dr. Kriti Bhargava, Coventry University (UK)
Host: Dr. Giulia Toti
The widespread adoption of Internet of Things paradigm has led to an abundance of unstructured data. Accurate and timely analysis of the data is imperative to extract meaningful patterns and gain insights into the data. Consequently, several machine learning techniques have been proposed for the analysis of data, to date. While supervised learning techniques rely on prior application knowledge and use pre-defined labels to classify data into groups, unsupervised learning techniques rely on identification of natural groupings in the data. Clustering is one of the most popular unsupervised learning techniques that uses a set of feature vectors to group data based on variability and dissimilarity metrics. This talk discusses the fundamentals of clustering and provides an overview of 3 main clustering approaches – partitional, hierarchical and density-based. The benefits and limitations of each approach are discussed using a representative algorithm.
Dr. Kriti Bhargava received her Bachelor’s (Hons.) and Master’s in Technology (dual degrees) in Computer Science from The LNM Institute of Information Technology, India, in 2014, and a Ph.D. degree in Computer Science from Waterford Institute of Technology, Ireland, in 2019. Dr. Bhargava is a Research Fellow with the School of Computing, Electronics and Mathematics at Coventry University, UK. Her research interests include edge computing, sensor networks, machine learning and wireless networking. She is particularly interested in the application of concepts of edge computing and machine learning for development of intelligent wireless sensor networks.