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Method for Appropriate Generalization in a Taxonomy

Student: Vlasov Aleksandr

Supervisor: Boris Mirkin

Faculty: Faculty of Computer Science

Educational Programme: Data Science (Master)

Final Grade: 9

Year of Graduation: 2019

This project considers a recently proposed method for maximally parsimonious generalization of fuzzy sets in taxonomies. The method is modified to the maximum likelihood criterion. A software is developed to support the computation, including a program for graphic visualization. The method applies to a collection of 26000 research papers in Data Science published over the past 20 years, using a taxonomy of Data Science developed earlier. The method of Annotated Suffix Tree applies to compute relevance indices between the papers and keywords (topics corresponding to terminal nodes of the taxonomy). This data is used to find fuzzy clusters of keywords - these clusters then are parsimoniously generalized with the developed software. Probabilities of emergence and loss of meanings in the taxonomy nodes are computed based on results obtained at 20\% random samples of papers. Our computational results show that the criteria of maximum parsimony and maximum likelihood are compatible. The found clusters and their generalizations broadly support earlier conclusions made over results of similar analyses of a Springer's collection of 18000 papers, bringing in much more detail.

Full text (added May 23, 2019)

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