A Study of Clustering Methods Using Visual Data
Name
Priit Danelson
Abstract
Cluster analysis is a widely used data analysis technique that can be applied by using sev-eral different algorithms. This thesis aims to give an overview of the working principles and specifics of the three most commonly used cluster analysis methods by applying hier-archical clustering, k-means clustering and self-organizing map algorithms on sample data. In addition to the description of working principles of the clustering algorithms, there is also a description of how the script used for applying the clustering methods is implement-ed and an explanation for why visual data or pictures are chosen as the sample data. The thesis also includes an analysis of the clustering results produced by the script.
Graduation Thesis language
Estonian
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Jaak Vilo
Defence year
2015