Clustering has become one of the most important processes of knowledge discovery from data in the era of big data. It explores and reveals the hidden patterns in the data, and provides insight into the natural groupings in the data. This PhD project aims to solve two existing problems of density-based clustering in order to efficiently identify the arbitrarily shaped and varied density clusters in high-dimensional data. I have investigated and designed different approaches for each problem. The effectiveness of these proposed approaches has been verified with extensive empirical evaluations on synthetic and real-world datasets.
History
Campus location
Australia
Principal supervisor
Kai Ming Ting
Additional supervisor 1
Mark Carman
Year of Award
2017
Department, School or Centre
Information Technology (Monash University Clayton)