Summary: ON THE IMPORTANCE AND INCORPORATION OF
ADDITIONAL KNOWLEDGE IN CLUSTER ANALYSIS
JAN FEYEREISL, BSc.
Thesis submitted to the University of Nottingham
for the degree of Doctor of Philosophy
To my family...
Analysis of data without labels is commonly subject to scrutiny by unsupervised ma-
chine learning techniques. Although abundant expert knowledge exists in many areas
where unlabelled data is examined, frequently such knowledge is not incorporated into
automatic analysis. Semi-supervised learning allows for the incorporation of additional
knowledge with the help of labels or constraints. However it is the field of supervised
learning and the recently proposed advanced paradigm of learning using privileged in-
formation that provides an intriguing concept of incorporating special type of additional
In this thesis we explore the question of importance and incorporation of such additional
knowledge within unsupervised learning. Our analysis is performed from four different
viewpoints, namely anomaly detection, cluster interpretation, visualisation and iden-