Algorithms for clustering data
Algorithms for clustering data
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A clustering algorithm using an evolutionary programming-based approach
Pattern Recognition Letters
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
The use of linked line segments for cluster representation and data reduction
Pattern Recognition Letters
On finding the number of clusters
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Maximum Variance Cluster Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Cluster Isolation Criterion Based on Dissimilarity Increments
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
IEEE Transactions on Knowledge and Data Engineering
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
A Shrinking-Based Clustering Approach for Multidimensional Data
IEEE Transactions on Knowledge and Data Engineering
A Modified K-Means Algorithm for Circular Invariant Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A voronoi diagram approach to autonomous clustering
DS'06 Proceedings of the 9th international conference on Discovery Science
Scale-based clustering using the radial basis function network
IEEE Transactions on Neural Networks
Feature selection using genetic algorithm and cluster validation
Expert Systems with Applications: An International Journal
Minimum spanning tree based split-and-merge: A hierarchical clustering method
Information Sciences: an International Journal
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Clustering is an important unsupervised learning technique widely used to discover the inherent structure of a given data set. Some existing clustering algorithms uses single prototype to represent each cluster, which may not adequately model the clusters of arbitrary shape and size and hence limit the clustering performance on complex data structure. This paper proposes a clustering algorithm to represent one cluster by multiple prototypes. The squared-error clustering is used to produce a number of prototypes to locate the regions of high density because of its low computational cost and yet good performance. A separation measure is proposed to evaluate how well two prototypes are separated. Multiple prototypes with small separations are grouped into a given number of clusters in the agglomerative method. New prototypes are iteratively added to improve the poor cluster separations. As a result, the proposed algorithm can discover the clusters of complex structure with robustness to initial settings. Experimental results on both synthetic and real data sets demonstrate the effectiveness of the proposed clustering algorithm.