An Exploration of Methodologies to Improve Semi-supervised Hierarchical Clustering with Knowledge-Based -Constraint Abeer Aljohani 2019.pdf (8.43 MB)

An exploration of methodologies to improve semi-supervised hierarchical clustering with knowledge-based constraints

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thesis
posted on 23.12.2019 by Abeer Aljohani
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. [Continues.]

History

School

  • Science

Department

  • Computer Science

Publisher

Loughborough University

Rights holder

© Abeer Ahmed Aljohani

Publication date

2019

Notes

A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of the degree of Doctor of Philosophy of Loughborough University.

Language

en

Supervisor(s)

Eran Edirisinghe ; Christian Dawson ; Daphne Teck Ching Lai

Qualification name

PhD

Qualification level

Doctoral

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