Larkin, Andrew B. (1995) The development of artificial neural networks for the analysis of market research and electronic nose data. PhD thesis, University of Warwick.

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Abstract

This thesis details research carried out into the application of unsupervised neural
network and statistical clustering techniques to market research interview survey
analysis. The objective of the research was to develop mathematical mechanisms to
locate and quantify internal clusters within the data sets with definite commonality.
As the data sets being used were binary, this commonality was expressed in terms of
identical question answers. Unsupervised neural network paradigms are investigated,
along with statistical clustering techniques. The theory of clustering in a binary space
is also looked at.
Attempts to improve the clarity of output of Self-Organising Maps (SOM) consisted
of several stages of investigation culminating in the conception of the Interrogative
Memory Structure (lMS). IMS proved easy to use, fast in operation and consistently
produced results with the highest degree of commonality when tested against SOM,
Adaptive Resonance Theory (ART!) and FASTCLUS. ARTl performed well when
clusters were measured using general metrics. During the course of the research a
supervised technique, the Vector Memory Array (VMA), was developed. VMA was
tested against Back Propagation (BP) (using data sets provided by the Warwick
electronic nose project) and consistently produced higher classification accuracies.
The main advantage of VMA is its speed of operation - in testing it produced results
in minutes compared to hours for the BP method, giving speed increases in the
region of 100: 1.

Item Type:	Thesis (PhD)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Library of Congress Subject Headings (LCSH):	Neural networks (Computer science), Self-organizing maps, Marketing research -- Data processing, Chemical detectors -- Data processing
Official Date:	March 1995
Dates:	Date Event March 1995 Submitted
Institution:	University of Warwick
Theses Department:	School of Engineering
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Whitehouse, D. J. (David J.)
Sponsors:	Engineering and Physical Sciences Research Council (EPSRC); Parallax Management Consultancy Ltd.; The Management Consulting Group Ltd.
Extent:	ix, 191 pages
Language:	eng

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