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Construction of classifiers suitable for segmentation of clients
Hricová, Jana ; Antoch, Jaromír (advisor) ; Zvára, Karel (referee)
Title: Construction of classifiers suitable for segmentation of clients Author: Bc. Jana Hricová Department: Department of Probability and Mathematical Statistics Supervisor: prof. RNDr. Jaromír Antoch, CSc., Department of Probability and Mathematical Statistics Abstract: The master thesis discusses methods that are a part of the data analy- sis, called classification. In the thesis are presented classification methods used to construct tree like classifiers suitable for customer segmentation. Core methodo- logy that is discussed in our thesis is CART (Classification and Regression Trees) and then methodologies around ensemble models that use historical data to cons- truct classification and regression forests, namely Bagging, Boosting, Arcing and Random Forest. Here described methods were applied to real data from the field of customer segmentation and also to simulated data, both processed with RStudio software. Keywords: classification, tree like classifiers, random forests
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K-means method
Hricová, Jana ; Antoch, Jaromír (advisor) ; Legát, David (referee)
Title: k-means method Author: Jana Hricová Department: Department of Probability and Mathematical Statistics Supervisor: prof. RNDr. Jaromír Antoch, CSc., Department of Probability and Mathematical Statistics Abstract: This thesis deals with the statistical method k-means, which is a part of an extensive set of methods and algorithms designed for cluster analysis of data. Results of the cluster analysis are widely used in other scientific activities, but also in marketing, management or in insurance etc. Statistical methods for cluster analysis are creating clusters from analyzed datasets, which consist of similar objects. Similarity of two objects is expressed by dis-/similarity measure. The aim of this thesis was to introduce the k-means algorithm. This is a non- hierarchical method with given number of output clusters as input. We have applied this algorithm in the enviroment of mathematical software Matlab on simulated and real data and have interpreted the results using graphical and numerical outputs. Keywords: k-means, cluster analysis, dissimilarity measure, silhouette
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