A Branch and Bound Clustering Algorithm
Warren L. G. Koontz, P. M. Narendra, Keinosuke Fukunaga · IEEE Transactions on Computers · 1975
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