Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.760023
Title: Automated data classification using feature weighted self-organising map (FWSOM)
Author: Ahamd Usman, Aliyu
ISNI:       0000 0004 7432 0402
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2018
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Abstract:
The enormous increase in the production of electronic data in today's information era has led to more challenges in analysing and understanding of the data. The rise in the innovations of technology devices, computers and the Internet has made it much easier to collect and store different kind of data ranging from personal, medical, financial, and scientific data. The growth in the amount of the generated data has introduced the term “Big Data” to describe this extremely high-dimensional and yet complex data. Making sense of the generated data sets is of great importance for the discovery of meaningful information that can be used to support decision making. Data mining techniques have been designed as a process for ex-ploring these data sets to extract meaning for decision making. An essential phase of the data mining procedure is the data transformation that involves the selection of input parameters. Selecting the right input parameters has a great impact on the performance of machine learning algorithms. Currently, there are existing manual statistical methods that are used for this task, but these are difficult to use, time consuming and require an expert. Automated data analysis is the initial step to relieve this burden from humans, through the provision of a systematic procedure of inspecting, transforming and modelling data for knowledge discovery. This project presents a novel method that exploits the power of self-organization for a sys-tematic procedure of conducting and inspecting data classification, with the identification of input parameters that are important for the process. The developed method can be used on different classification problems with practical application in various areas such as health con-dition monitoring in health care, machinery fault detection and analysis, and financial instrument analysis among others.
Supervisor: Starkey, Andrew ; Bannerman, Campbell M. Sponsor: University of Aberdeen
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.760023  DOI: Not available
Keywords: Automatic classification ; Data mining ; Neural networks (Computer science) ; Self-organizing maps
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