10.4225/03/592266e0db4cd
YE ZHU
YE
ZHU
Efficient Identification of Arbitrarily Shaped and Varied Density Clusters in High-dimensional Data
Monash University
2017
Varied densities
Density-based clustering
Density-ratio
Scaling
Shared Subspaces
Subspace Clustering
Knowledge Representation and Machine Learning
Pattern Recognition and Data Mining
2017-05-22 04:19:41
Thesis
https://bridges.monash.edu/articles/thesis/Efficient_Identification_of_Arbitrarily_Shaped_and_Varied_Density_Clusters_in_High-dimensional_Data/5023628
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.