Title page for ETD etd-10172011-211435

Document Type Doctoral Thesis
Author Graaff, Alexander Jakobus
Email agraaff@cs.up.ac.za
URN etd-10172011-211435
Document Title A local network neighbourhood artificial immune system
Degree PhD
Department Computer Science
Advisor Name Title
Prof A P Engelbrecht Supervisor
  • klonale seleksie
  • kunsmatige immuun netwerke
  • immuun netwerk topologieŽ
  • groepering van dinamiese data
  • affiniteit volwassewording
  • somatiese hiper mutasie
  • kunsmatige limfosiete
  • data groepering
  • affinity maturation
  • clonal selection
  • artificial lymphocytes
  • somatic hyper mutation
  • groepering prestasie maatreŽls
  • data clustering
  • dinamiese groepering
Date 2011-09-06
Availability unrestricted
As information is becoming more available online and will forevermore be part of any business, the true value of the large amounts of stored data is in the discovery of hidden and unknown relations and connections or traits in the data. The acquisition of these hidden relations can influence strategic decisions which have an impact on the success of a business. Data clustering is one of many methods to partition data into different groups in such a way that data patterns within the same group share some common trait compared to patterns across different groups. This thesis proposes a new artificial immune model for the problem of data clustering. The new model is inspired by the network theory of immunology and differs from its network based predecessor models in its formation of artificial lymphocyte networks. The proposed model is first applied to data clustering problems in stationary environments. Two different techniques are then proposed which enhances the proposed artificial immune model to dynamically determine the number of clusters in a data set with minimal to no user interference. A technique to generate synthetic data sets for data clustering of non-stationary environments is then proposed. Lastly, the original proposed artificial immune model and the enhanced version to dynamically determine the number of clusters are then applied to generated synthetic non-stationary data clustering problems. The influence of the parameters on the clustering performance is investigated for all versions of the proposed artificial immune model and supported by empirical results and statistical hypothesis tests.

AFRIKAANS: Soos wat inligting meer aanlyn toeganglik raak en vir altyd meer deel vorm van enige besigheid, is die eintlike waarde van groot hoeveelhede data in die ontdekking van verskuilde en onbekende verwantskappe en konneksies of eienskappe in die data. Die verkryging van sulke verskuilde verwantskappe kan die strategiese besluitneming van ín besigheid beinvloed, wat weer ín impak het op die sukses van ín besigheid. Data groepering is een van baie metodes om data op so ín manier te groepeer dat data patrone wat deel vorm van dieselfde groep ín gemeenskaplike eienskap deel in vergelyking met patrone wat verspreid is in ander groepe. Hierdie tesis stel ín nuwe kunsmatige immuun model voor vir die probleem van data groepering. Die nuwe model is geinspireer deur die netwerk teorie in immunologie en verskil van vorige netwerk gebaseerde modelle deur die model se formasie van kunsmatige limfosiet netwerke. Die voorgestelde model word eers toegepas op data groeperingsprobleme in statiese omgewings. Twee verskillende tegnieke word dan voorgestel wat die voorgestelde kunsmatige immuun model op so ín manier verbeter dat die model die aantal groepe in ín data stel dinamies kan bepaal met minimum tot geen gebruiker invloed. ín Tegniek om kunsmatige data stelle te genereer vir data groepering in dinamiese omgewings word dan voorgestel. Laastens word die oorspronklik voorgestelde model sowel as die verbeterde model wat dinamies die aantal groepe in ín data stel kan bepaal toegepas op kunsmatig genereerde dinamiese data groeperingsprobleme. Die invloed van die parameters op die groepering prestasie is ondersoek vir alle weergawes van die voorgestelde kunsmatige immuun model en word toegelig deur empiriese resultate en statistiese hipotese toetse.

© 2011 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.Please cite as follows:

Graaff, AJ 2011, A local network neighbourhood artificial immune system, PhD thesis, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-10172011-211435 / >


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  00front.pdf 117.97 Kb 00:00:32 00:00:16 00:00:14 00:00:07 < 00:00:01
  01chapters1-2.pdf 536.04 Kb 00:02:28 00:01:16 00:01:07 00:00:33 00:00:02
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