A multi-prototype clustering algorithm

@article{Liu2009AMC,
  title={A multi-prototype clustering algorithm},
  author={Manhua Liu and Xudong Jiang and Alex Chichung Kot},
  journal={Pattern Recognit.},
  year={2009},
  volume={42},
  pages={689-698}
}

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References

SHOWING 1-10 OF 42 REFERENCES
On the Two-level Hybrid Clustering Algorithm
TLDR
The hybrid clustering approach developed here represents the original data set using a smaller set of prototype vectors (cluster means), which allows efficient use of a clustering algorithm to divide the prototype into groups at the first level.
OPTICS: ordering points to identify the clustering structure
TLDR
A new algorithm is introduced for the purpose of cluster analysis which does not produce a clustering of a data set explicitly; but instead creates an augmented ordering of the database representing its density-based clustering structure.
C HAMELEON : A Hierarchical Clustering Algorithm Using Dynamic Modeling
TLDR
A novel hierarchical clustering algorithm called C HAMELEON that measures the similarity of two clusters based on a dynamic model and can discover natural clusters that many existing state of the art clustering algorithms fail to find.
Cluster center initialization algorithm for K-means clustering
CURE: an efficient clustering algorithm for large databases
TLDR
This work proposes a new clustering algorithm called CURE that is more robust to outliers, and identifies clusters having non-spherical shapes and wide variances in size, and demonstrates that random sampling and partitioning enable CURE to not only outperform existing algorithms but also to scale well for large databases without sacrificing clustering quality.
A hybrid clustering procedure for concentric and chain-like clusters
TLDR
A hybrid clustering algorithm, based on the concepts of multilevel theory, which is nonhierarchical at the first level and hierarchical from second level onwards, to cluster data sets having (i) chain-like clusters and (ii) concentric clusters is described.
Combining Partitional and Hierarchical Algorithms for Robust and Efficient Data Clustering with Cohesion Self-Merging
TLDR
A two-phase clustering algorithm, called cohesion-based self-merging (abbreviated as CSM), which runs in time linear to the size of input data set and is shown to be able to cluster the data sets of arbitrary shapes very efficiently and provide better clustering results than those by prior methods.
A Voronoi Diagram Approach to Autonomous Clustering
TLDR
An agglomerative clustering algorithm which accesses density information by constructing a Voronoi diagram for the input sample and clearly outperforms k-means algorithm on data conforming to its underlying assumptions.
Data clustering: a review
TLDR
An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
...
...