Journal of the ACM (JACM)
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
Computational experience on four algorithms for the hard clustering problem
Pattern Recognition Letters
Convergent design of piecewise linear neural networks
Neurocomputing
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Recent Developments in Pattern Recognition
IEEE Transactions on Computers
Pattern Recognition and Image Processing
IEEE Transactions on Computers
Modified global k-means algorithm for minimum sum-of-squares clustering problems
Pattern Recognition
Data Clustering with Semi-binary Nonnegative Matrix Factorization
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
MAkE: Multiobjective algorithm for k-way equipartitioning of a point set
Applied Soft Computing
Expert Systems with Applications: An International Journal
Genetic algorithm for text clustering based on latent semantic indexing
Computers & Mathematics with Applications
The hyperbolic smoothing clustering method
Pattern Recognition
Multibody structure-and-motion segmentation by branch-and-bound model selection
IEEE Transactions on Image Processing
Fast modified global k-means algorithm for incremental cluster construction
Pattern Recognition
EURASIP Journal on Wireless Communications and Networking - Special issue on theoretical and algorithmic foundations of wireless ad hoc and sensor networks
Energy-efficient collaborative tracking in wireless sensor networks
International Journal of Sensor Networks
Extensions to the repetitive branch and bound algorithm for globally optimal clusterwise regression
Computers and Operations Research
Automatic Topic Ontology Construction Using Semantic Relations from WordNet and Wikipedia
International Journal of Intelligent Information Technologies
| Hi-index | 14.99 |
The problem of clustering N objects into M classes may be viewed as a combinatorial optimization algorithm. In the literature on clustering, iterative hill-climbing techniques are used to find a locally optimum classification. In this paper, we develop a clustering algorithm based on the branch and bound method of combinatorial optimization. This algorithm determines the globally optimum classification and is computationally efficient