Algorithms for clustering data
Algorithms for clustering data
A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm
Pattern Recognition Letters
Data mining: concepts and techniques
Data mining: concepts and techniques
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A Genetic Algorithm Using Hyper-Quadtrees for Low-Dimensional K-means Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast mining of distance-based outliers in high-dimensional datasets
Data Mining and Knowledge Discovery
Modified global k-means algorithm for minimum sum-of-squares clustering problems
Pattern Recognition
Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters
IEEE Transactions on Knowledge and Data Engineering
A new measure of uncertainty based on knowledge granulation for rough sets
Information Sciences: an International Journal
MGRS: A multi-granulation rough set
Information Sciences: an International Journal
Fast global k-means clustering using cluster membership and inequality
Pattern Recognition
Fast modified global k-means algorithm for incremental cluster construction
Pattern Recognition
Parallel Spectral Clustering in Distributed Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel ant-based clustering algorithm using the kernel method
Information Sciences: an International Journal
Model order selection for multiple cooperative swarms clustering using stability analysis
Information Sciences: an International Journal
Clustering and selecting suppliers based on simulated annealing algorithms
Computers & Mathematics with Applications
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Integration of particle swarm optimization and genetic algorithm for dynamic clustering
Information Sciences: an International Journal
Gene transposon based clone selection algorithm for automatic clustering
Information Sciences: an International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient stochastic algorithms for document clustering
Information Sciences: an International Journal
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
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The fast global k-means (FGKM) clustering algorithm is one of the most effective approaches for resolving the local convergence of the k-means clustering algorithm. Numerical experiments show that it can effectively determine a global or near global minimizer of the cost function. However, the FGKM algorithm needs a large amount of computational time or storage space when handling large data sets. To overcome this deficiency, a more efficient FGKM algorithm, namely FGKM+A, is developed in this paper. In the development, we first apply local geometrical information to describe approximately the set of objects represented by a candidate cluster center. On the basis of the approximate description, we then propose an acceleration mechanism for the production of new cluster centers. As a result of the acceleration, the FGKM+A algorithm not only yields the same clustering results as that of the FGKM algorithm but also requires less computational time and fewer distance calculations than the FGKM algorithm and its existing modifications. The efficiency of the FGKM+A algorithm is further confirmed by experimental studies on several UCI data sets.