Clustering methods with a focus on self-organising maps and an implementation on retail bank transactional data

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dc.contributor.advisorSteel, S. J.en_ZA
dc.contributor.authorEnslin, Chrismarieen_ZA
dc.contributor.otherStellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.en_ZA
dc.date.accessioned2018-11-26T19:17:11Z
dc.date.accessioned2018-12-10T06:35:59Z
dc.date.available2018-11-26T19:17:11Z
dc.date.available2018-12-10T06:35:59Z
dc.date.issued2018-12
dc.descriptionThesis (MCom)--Stellenbosch University, 2020.en_ZA
dc.description.abstractENGLISH SUMMARY : The aims of this study is to provide an overview of traditional clustering methods, as well as introduce and discuss self-organising maps (SOMs) in detail. This study wants to convince the reader of the usefulness of self-organising maps as a dimension reduction tool. The batch SOMs algorithm was found to be the most appropriate SOM to use in practice, together with random initialisation of the prototypes. Ward linkage hierarchical clustering was found to perform the best on multivariate Gaussian simulated data and it was also found to be the most appropriate traditional clustering method to fit on top of the SOM. Banking transactional data was investigated for client behavioural clusters and the clusters of lower socio-economic class clients, technologically sophisticated clients, older and more traditional clients and low financial activity clients were found. These clusters emerged consistently throughout 9 different data samples.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING : Die doel van hierdie studie is om ’n oorsig oor tradisionele groeperings metodes saam te stel, sowel as om selforganiserende kaarte (SOK) (“self-organising maps”) te bespreek. Hierdie studie wil die leser oortuig van die bruikbaarheid van SOK as ’n dimensie-vermindering tegniek. Die bondel-SOK algoritme is die metode wat in die praktyk aanbeveel word, saam met lukrake inisialisering van die prototipes. Ward-koppeling (“Ward linkage”) hiërargiese groepering het die beste presteer op multivariaat-Gaussies gesimuleerde data. In hierdies studie is ook gevind dat Ward-koppeling die mees toepaslike tradisionele groeperingsmetode was om bo-op die SOK aan te wend. Data uit die transaksionele bank omgewing is ondersoek om kliënt gedragsgroepe te vind. Hierdie gedragsgroepe is geïdentifiseer as laer sosio-ekonomiese klas kliënte, tegnologies gesofistikeerde kliënte, ouer en meer tradisionele kliënte en ook ’n groep met lae finansiële aktiwiteit. Die ontleding het hierdie groepe konsekwent oor 9 verskillende datastelle geïdentifiseer.af_ZA
dc.description.versionMastersen_ZA
dc.format.extentxii, 159 pages ; illustrations, includes annexures
dc.identifier.urihttp://hdl.handle.net/10019.1/105186
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subjectUnsupervised learningen_ZA
dc.subjectSelf-organising mapsen_ZA
dc.subjectCluster analysisen_ZA
dc.subjectK-means clusteringen_ZA
dc.subjectK-medoids clusteringen_ZA
dc.subjectHierarchical clusteringen_ZA
dc.subjectBig data -- Cluster analysisen_ZA
dc.subjectUCTD
dc.titleClustering methods with a focus on self-organising maps and an implementation on retail bank transactional dataen_ZA
dc.typeThesisen_ZA
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