Extending self-organizing maps with ranking awareness
Rozšíření self-organizing maps o ranking awareness
bachelor thesis (DEFENDED)
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Permanent link
http://hdl.handle.net/20.500.11956/176049Identifiers
Study Information System: 236648
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- Kvalifikační práce [10932]
Author
Advisor
Referee
Lokoč, Jakub
Faculty / Institute
Faculty of Mathematics and Physics
Discipline
General Computer Science
Department
Department of Software Engineering
Date of defense
12. 9. 2022
Publisher
Univerzita Karlova, Matematicko-fyzikální fakultaLanguage
English
Grade
Good
Keywords (Czech)
self-organizing map|relevence feedback|known-item searchKeywords (English)
self-organizing maps|multicriterial optimization|ranking awarenessTitle: Extending Self-organizing Maps with Ranking Awareness Author: Kyung Won Park Department: Department of Software Engineering Supervisor: Mgr. Ladislav Peska, Ph.D., Department of Software Engineering Abstract: The self-organizing map (SOM) is a powerful clustering algorithm which takes high- dimensional data as the input and produces a low-dimensional representation of the data. The SOM provides useful insights into the given data by recognizing similar input vectors and clustering them. However, they take into account only the local similarity of the input data, as opposed to relevance (any external ranking). In this paper, we propose two ranking-aware variants of the SOM in an effort to tackle this issue and incorporate evaluation metrics to evaluate our results. Keywords: self-organizing map, relevence feedback, known-item search