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}
}
View via Publisher
ccc.inaoep.mx
66 Citations
Highly Influential Citations
6
Background Citations
20
Methods Citations
20

Figures and Tables from this paper

66 Citations

Citation Type

Citation Type

A multi-prototype clustering algorithm based on minimum spanning tree
TLDR
A minimum spanning tree (MST) based multi-prototype clustering algorithm that can deal with datasets consisting of clusters with different shapes, sizes and densities is proposed.
Guided fuzzy clustering with multi-prototypes
A new fuzzy clustering algorithm using multi-prototype representation of clusters is proposed in this paper to discover clusters with arbitrary shapes and sizes. Intra-cluster non-consistency and…
A new multi-prototype based clustering algorithm
TLDR
A new incremental k-means clustering algorithm is designed to determine the propriate prototype number automatically and a new indicator is presented to judge whether the number of prototype is appropriate in the split stage.
Online Cluster Prototype Generation for the Gravitational Clustering Algorithm
TLDR
An on-line cluster prototype generation mechanism for the Gravitational Clustering algorithm that uses the gravitational system dynamic and the inherent hierarchical property of the gravitational algorithm for determining some summarized prototypes of clusters at the same time the gravitational clustering algorithm is finding such clusters.
A Three-Level Optimization Model for Nonlinearly Separable Clustering
TLDR
A three-level optimization model for nonlinearly separable clustering is proposed which divides the clustering problem into three sub-problems: a linearly separables clustering on the object set, a nonlinear separable clusters on the cluster set and an ensemble cluster on the partition set.
Prototype Propagation Clustering Based on Large Margin
TLDR
This paper draws large margin theory into the assignment step and proposes a prototype propagation clustering algorithm which gradually assigns the instances to the clusters.
A fast hybrid clustering technique based on local nearest neighbor using minimum spanning tree
A new clustering algorithm based on Voronoi diagram
TLDR
A new clustering algorithm which is based on Voronoi diagram is introduced which performs better in discovering the complex clusters and able to detect the outliers.
A distance based clustering method for arbitrary shaped clusters in large datasets
K-Multiple-Means: A Multiple-Means Clustering Method with Specified K Clusters
TLDR
This paper proposes a K-Multiple-Means (KMM) method to group the data points with multiple sub-cluster means into specified k clusters and formalizes the multiple-means clustering problem as an optimization problem and updates the partitions of m sub-Cluster means and k clusters by an alternating optimization strategy.
...
...

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.
A new approach to clustering data with arbitrary shapes
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.
...
...