- Tomasz Walkowiak ORCID:orcid.org/0000-0002-7749-425119 &
- Mateusz Gniewkowski ORCID:orcid.org/0000-0002-0620-812320
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Abstract
Topic modeling is a method for discovery of topics (groups of words). In this paper we focus on clustering documents in a topic space obtained using theMALLET tool. We tested several different distance measures with two clustering algorithm (spectral clustering,agglomerative hierarchical clustering) and described those that served better (cosine distance,correlation distance,bhattacharyya distance) than the Euclidean metric for k-means algorithm. For evaluation purpose we usedAdjusted Mutual Information (AMI) score. The need for such experiments comes from the difficulty of choosing appropriate grouping methods for the given data, which is specific in our case.
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Acknowledgements
The work was funded by the Polish Ministry of Science and Higher Education within CLARIN-PL Research Infrastructure.
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Authors and Affiliations
Faculty of Electronics, Wrocław University of Science and Technology, Wrocław, Poland
Tomasz Walkowiak
Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland
Mateusz Gniewkowski
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Correspondence toTomasz Walkowiak.
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Wrocław University of Science and Technology, Wroclaw, Poland
Wojciech Zamojski
Wrocław University of Science and Technology, Wroclaw, Poland
Jacek Mazurkiewicz
Wrocław University of Science and Technology, Wroclaw, Poland
Jarosław Sugier
Wrocław University of Science and Technology, Wroclaw, Poland
Tomasz Walkowiak
Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Janusz Kacprzyk
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Walkowiak, T., Gniewkowski, M. (2020). Distance Measures for Clustering of Documents in a Topic Space. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) Engineering in Dependability of Computer Systems and Networks. DepCoS-RELCOMEX 2019. Advances in Intelligent Systems and Computing, vol 987. Springer, Cham. https://doi.org/10.1007/978-3-030-19501-4_54
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