A simulated annealing algorithm for the clustering problem
Pattern Recognition
Cluster analysis and mathematical programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
ACM Computing Surveys (CSUR)
An Interior Point Algorithm for Minimum Sum-of-Squares Clustering
SIAM Journal on Scientific Computing
Variable Neighborhood Decomposition Search
Journal of Heuristics
A Branch and Bound Clustering Algorithm
IEEE Transactions on Computers
Performance evaluation of density-based clustering methods
Information Sciences: an International Journal
The global kernel k-means algorithm for clustering in feature space
IEEE Transactions on Neural Networks
The hyperbolic smoothing clustering method
Pattern Recognition
Fast global k-means clustering using cluster membership and inequality
Pattern Recognition
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Fast modified global k-means algorithm for incremental cluster construction
Pattern Recognition
Weight selection in W-K-means algorithm with an application in color image segmentation
Computers & Mathematics with Applications
Texture classification based on contourlet subband clustering
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
An architecture for component-based design of representative-based clustering algorithms
Data & Knowledge Engineering
Improved Parameterless K-Means: Auto-Generation Centroids and Distance Data Point Clusters
International Journal of Information Retrieval Research
Applying clustering and ensemble clustering approaches to phishing profiling
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
Feature selection using misclassification counts
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Fast global k-means clustering based on local geometrical information
Information Sciences: an International Journal
A fast partitioning algorithm and its application to earthquake investigation
Computers & Geosciences
New and efficient DCA based algorithms for minimum sum-of-squares clustering
Pattern Recognition
Learning descriptive visual representation by semantic regularized matrix factorization
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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k-Means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and inefficient for solving clustering problems in large data sets. Recently, a new version of the k-means algorithm, the global k-means algorithm has been developed. It is an incremental algorithm that dynamically adds one cluster center at a time and uses each data point as a candidate for the k-th cluster center. Results of numerical experiments show that the global k-means algorithm considerably outperforms the k-means algorithms. In this paper, a new version of the global k-means algorithm is proposed. A starting point for the k-th cluster center in this algorithm is computed by minimizing an auxiliary cluster function. Results of numerical experiments on 14 data sets demonstrate the superiority of the new algorithm, however, it requires more computational time than the global k-means algorithm.