Masters Degrees (Statistics and Actuarial Science)
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Browsing Masters Degrees (Statistics and Actuarial Science) by Subject "Big data -- Cluster analysis"
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- ItemClustering methods with a focus on self-organising maps and an implementation on retail bank transactional data(Stellenbosch : Stellenbosch University, 2018-12) Enslin, Chrismarie; Steel, S. J.; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.ENGLISH 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.