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Title: Majority problems in distributed systems and clustering in structured graphs
Author: Hamilton, D. D.
ISNI:       0000 0004 6423 0219
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2017
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This thesis focuses on the study of various algorithms for Distributed Computing and Machine Learning research areas. More precisely, the work within contains research into various communication protocols in different settings of Distributed Computing, accompanied by relevant analysis on protocol performance in time and space. These protocols are designed to operate in analogous environments using different models for communication, primarily population protocol and random walk variants. In our settings we aim to use as minimal memory as possible, achieving light weight protocols that are powerful in their capabilities and randomized as well as deterministic in nature. We also propose a novel technique of verification which enables multi-step protocols to work in synergy. These protocols generally never terminate, but converge and are difficult to disseminate results throughout the network to be used in dependent processes. With the verification technique proposed, protocols can become adaptive and stacked into a chain of dependent processes. We also provide experimental analysis of a subarea of Machine Learning, unsupervised clustering algorithms. Gaining inspiration from the agglomerative nature and techniques defined in classical hierarchical clustering as well as the Phylogenetic tree building methods, we provide a comprehensive study and evaluation of new method to agglomeratively combine `similar' data into clusters based on the general consensus of taxonomy and evaluation of clustering mechanisms.
Supervisor: Martin, R. ; Gasieniec, L. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral