Evaluating Multi-view Kernel Clustering Algorithms

2018-2019 Summer
Faculty Department of Project Supervisor: 
Faculty of Arts & Social Sciences
Number of Students: 

Multi-view clustering is a clustering paradigm that combınes available multi-view feature information to group subjects into clusters. Multi-view kernel clustering employs kernel functions to represent each view. Kernel functions allow solving the problem in a higher-dimensional space. There have been several multi-view kernel clustering approaches developed within the last decade. The project aims to systematically evaluate their performance on multiple real datasets.

Related Areas of Project: 
Computer Science and Engineering
Industrial Engineering