Exploratory database visualisation: the application and assessment of data and dimensionality reduction

  • Philip J. Barrett

    Student thesis: Doctoral ThesisDoctor of Philosophy


    This thesis describes the development of a complete data visualisation system for
    large tabular databases, such as those commonly found in a business environment.
    A state-of-the-art 'cyberspace cell' data visualisation technique was investigated and a powerful visualisation system using it was implemented. Although allowing databases to be explored and conclusions drawn, it had several drawbacks, the majority of which were due to the three-dimensional nature of the visualisation.
    A novel two-dimensional generic visualisation system, known as MADEN, was then
    developed and implemented, based upon a 2-D matrix of 'density plots'. MADEN
    allows an entire high-dimensional database to be visualised in one window, while
    permitting close analysis in 'enlargement' windows. Selections of records can be
    made and examined, and dependencies between fields can be investigated in detail.
    MADEN was used as a tool for investigating and assessing many data processing
    algorithms, firstly data-reducing (clustering) methods, then dimensionality-reducing techniques. These included a new 'directed' form of principal components analysis, several novel applications of artificial neural networks, and discriminant analysis techniques which illustrated how groups within a database can be separated.
    To illustrate the power of the system, MADEN was used to explore customer databases from two financial institutions, resulting in a number of discoveries which would be of interest to a marketing manager. Finally, the database of results from the 1992 UK Research Assessment Exercise was analysed. Using MADEN allowed both universities and disciplines to be graphically compared, and supplied some startling revelations, including empirical evidence of the 'Oxbridge factor'.
    Date of AwardSept 1995
    Original languageEnglish
    SupervisorDavid Bounds (Supervisor)


    • cyberspace
    • neural networks
    • Kohonen map
    • directed PCA
    • RAE92

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