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\iteman{io-port 06106045}
\itemau{Xi\c{e}ski, Tomasz; Nowak-Brzezi\'nska, Agnieszka; Wakulicz-Deja, Alicja}
\itemti{Density-based method for clustering and visualization of complex data.}
\itemso{Yao, JingTao (ed.) et al., Rough sets and current trends in computing. 8th international conference, RSCTC 2012, Chengdu, China, August 17--20, 2012. Proceedings. Berlin: Springer (ISBN 978-3-642-32114-6/pbk). Lecture Notes in Computer Science 7413. Lecture Notes in Artificial Intelligence, 142-149 (2012).}
\itemab
Summary: In this paper the topic of clustering and visualization of the data structure is discussed. Authors review currently found in literature algorithmic solutions ([3], [5]) that deal with clustering large volumes of data, focusing on their disadvantages and problems. What is more the authors introduce and analyze a density-based algorithm OPTICS (Ordering Points To Identify the Clustering Structure) as a method for clustering a real-world dataset about the functioning of transceivers of a cellular phone operator located in Poland. This algorithm is also presented as an relatively easy way for visualization of the data's inner structure, relationships and hierarchies. The whole analysis is performed as a comparison to the well-known and described DBSCAN algorithm.
\itemrv{~}
\itemcc{}
\itemut{cluster visualization; clustering; OPTICS; DBSCAN}
\itemli{doi:10.1007/978-3-642-32115-3\_16}
\end