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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
Comparison Of Modern Clustering Algorithms For Two-Dimensional Data |
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Authors: |
Martin Kotyrba, Eva Volna, Zuzana Kominkova Oplatkova |
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Published in:
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(2014).ECMS 2014 Proceedings edited
by: Flaminio Squazzoni,
Fabio Baronio, Claudia Archetti,
Marco Castellani European Council for
Modeling and Simulation. doi:10.7148/2014 ISBN:
978-0-9564944-8-1 28th
European Conference on Modelling and Simulation, Brescia,
Italy, May 27th – 30th,
2014 |
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Citation
format: |
Martin
Kotyrba, Eva Volna, Zuzana Kominkova Oplatkova (2014). Comparison
Of Modern Clustering Algorithms For Two-Dimensional Data, ECMS 2014
Proceedings edited by: Flaminio Squazzoni,
Fabio Baronio, Claudia Archetti,
Marco Castellani European Council for Modeling and Simulation. doi:10.7148/2014-0346 |
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DOI: |
http://dx.doi.org/10.7148/2014-0346 |
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Abstract: |
Cluster analysis or
clustering is a task of grouping a set of objects in such a way that objects
in the same group (called a cluster) are more similar (in some sense or another)
to each other than to those in other groups (clusters). It is the main task
of exploratory data mining and a common technique for statistical data
analysis used in many fields, including machine learning, pattern recognition,
image analysis, information retrieval, and bioinformatics.The topic of this paper is modern methods
of clustering. The paper describes the theory needed to understand the
principle of clustering and descriptions of algorithms used with clustering, followed
by a comparison of the chosen methods. |
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