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Title: An exploration of methodologies to improve semi-supervised hierarchical clustering with knowledge-based constraints
Author: Aljohani, Abeer
ISNI:       0000 0004 8510 0333
Awarding Body: Loughborough University
Current Institution: Loughborough University
Date of Award: 2019
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Clustering algorithms with constraints (also known as semi-supervised clustering algorithms) have been introduced to the field of machine learning as a significant variant to the conventional unsupervised clustering learning algorithms. They have been demonstrated to achieve better performance due to integrating prior knowledge during the clustering process, that enables uncovering relevant useful information from the data being clustered. However, the research conducted within the context of developing semi-supervised hierarchical clustering techniques are still an open and active investigation area. Majority of current semi-supervised clustering algorithms are developed as partitional clustering (PC) methods and only few research efforts have been made on developing semi-supervised hierarchical clustering methods. The aim of this research is to enhance hierarchical clustering (HC) algorithms based on prior knowledge, by adopting novel methodologies.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: Semi-supervised Hierarchical Clustering ; Hierarchical Clustering ; triple-wise relative constraints