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Data Mining Methods for Analyzing Feedback in E-commerce

Student: Khurakay Dolma

Supervisor: Olga A. Tsukanova

Faculty: Graduate School of Business

Educational Programme: Electronic Business (Master)

Year of Graduation: 2020

This master’s thesis explores data mining methods for analyzing feedback in e-commerce. The first chapter presents the task of feedback in the sphere of electronic commerce: the role of electronic commerce in the modern world and in Russia is investigated; a definition of the concept of feedback, its features and its role in e-commerce are considered. The second chapter is devoted to a theoretical study of data mining methods: the basic definitions and a classification of tasks are given; the methods are generalized in accordance with the tasks; the specifics of the application of a range of methods to the field of electronic commerce are considered. The third chapter shows the results of an experimental research of the application of data mining in to the problem of feedback analysis on the a test data set; a set of considered methods included classification methods, clustering methods (full communication method, genetic algorithm, ranking, hierarchical clustering method - graph algorithm), regression analysis. The results of the research can be of interest both for researchers in the field of data analysis, and for specialists in the field of electronic commerce.

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