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Erschienen in:
2010 | OriginalPaper | Buchkapitel
A Probabilistic MajorClust Variant for the Clustering of Near-Homogeneous Graphs
verfasst von : Oliver Niggemann, Volker Lohweg, Tim Tack
Erschienen in: KI 2010: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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Clustering remains a major topic in machine learning; it is used e.g. for document categorization, for data mining, and for image analysis. In all these application areas, clustering algorithms try to identify groups of related data in large data sets.
In this paper, the established clustering algorithm
MajorClust
([12]) is improved; making it applicable to data sets with few structure on the local scale—so called near-homogeneous graphs. This new algorithm
MCProb
is verified empirically using the problem of image clustering. Furthermore,
MCProb
is analyzed theoretically. For the applications examined so-far,
MCProb
outperforms other established clustering techniques.