In daily life as well as in different fields of science we encounter big, sometimes enormous volume of information. One look is enough for humans to distinguish the shapes of objects being of interest to us from a specific image. Intelligent machines, however, are still incapable of prompt and unerring distinguishing of objects in the image, due to the lack of universal algorithms which would work in every situation.
The objective of data clustering is a partition of data set into clusters of similar data. Objects in the data set may be e.g. bank customers, figures or things in a photograph, sick and healthy persons. A human being may effectively group only one- and two-dimensional data, while three-dimensional data may cause serious difficulties. The scale of the problem is intensified by the fact that the number of samples in real tasks may amount to thousands and millions. In the light of those facts it would be very useful to have algorithms for automatic data clustering. Operation of those algorithms would result in a fixed structure of data partition, i.e. location and shape of the clusters and membership degrees of each sample to each cluster. Data clustering is a complicated issue as the structures hidden in the data set may have any shapes and sizes. Moreover, the number of clusters is usually unknown. Unfortunately, the literature so far does not provide any algorithm which would work in the case of any shapes of clusters.
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© 2008 Springer-Verlag Berlin Heidelberg
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(2008). Data clustering methods. In: Computational Intelligence. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76288-1_8
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DOI: https://doi.org/10.1007/978-3-540-76288-1_8
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