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Clustering mixed mode data
(2021) Apostolaki, Eleftheria; Αποστολάκη, Ελευθερία; Athens University of Economics and Business, Department of Management Science and Technology; Karlis, Dimitrios
In the current thesis, the research problem that will be approached covers the clustering of mixed mode data (e.g. numeric, categorical, etc.), its benefits and applications. In Chapter 2, the literature review for clustering mixed mode data is detailed including the methodologies that will be used as part of the thesis and any additional methodologies that are available for this type of clustering according to the bibliography. In Chapter 3, a detailed overview and analysis is presented for the prostate cancer dataset on which the selected clustering methods (Kamila, K-Prototypes, Latent Variable Model) will be applied while in Chapter 4 the clustering results and their interpretation are provided. In chapter 5 that follows, the conclusions drawn from this research are described along with any future work required for the clustering of mixed mode data.