Abstract
In this work, the topic of applying clustering as a knowledge extraction method from real-world data is discussed. The authors propose hierarchical clustering and treemap visualization techniques for knowledge base representation in the context of medical knowledge bases, for which data mining techniques are successfully employed and may resolve different problems. The authors analyze the impact of different clustering parameters on the result of searching through such a structure. Particular attention was also given to clusters description. The authors examined how selected inter-cluster and inter-object similarity measures influence clusters representatives.
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Acknowledgement
This work is a part of the project “Exploration of rule knowledge bases” founded by the Polish National Science Centre (NCN: 2011/03/D/ST6/03027).
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Nowak-Brzezińska, A., Rybotycki, T., Simiński, R., Przybyła-Kasperek, M. (2016). Mining Medical Knowledge Bases. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_45
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DOI: https://doi.org/10.1007/978-3-319-45246-3_45
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