Graph Clustering Using Multiway Ratio Cut
GD '97 Proceedings of the 5th International Symposium on Graph Drawing
On the Nature of Structure and Its Identification
WG '99 Proceedings of the 25th International Workshop on Graph-Theoretic Concepts in Computer Science
Cluster Cores-Based Clustering for High Dimensional Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Constrained locally weighted clustering
Proceedings of the VLDB Endowment
Fuzzifying clustering algorithms: the case study of majorclust
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
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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.