Abstract
This work is focused on the matter of clustering complex data using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm and searching through such a structure. It presents related problems, focusing primarily on the aspect of choosing the initial parameters of the density based algorithm, as well as various ways of creating valid cluster representatives. What is more, the paper emphasizes the importance of the domain knowledge, as a factor which has a huge impact on the quality of the clustering. Carried out experiments allow to compare the efficiency of finding clusters relevant to the given question, depending on the way of how the cluster representatives were created.
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Wakulicz-Deja, A., Nowak-Brzezińska, A., Xięski, T. (2011). Efficiency of Complex Data Clustering. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds) Rough Sets and Knowledge Technology. RSKT 2011. Lecture Notes in Computer Science(), vol 6954. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24425-4_80
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DOI: https://doi.org/10.1007/978-3-642-24425-4_80
Publisher Name: Springer, Berlin, Heidelberg
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