Algorithms for clustering data
Algorithms for clustering data
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
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
Clustering Algorithms
Mathematical Programming in Data Mining
Data Mining and Knowledge Discovery
Design of hybrids for the minimum sum-of-squares clustering problem
Computational Statistics & Data Analysis
A Branch and Bound Clustering Algorithm
IEEE Transactions on Computers
Modified global k-means algorithm for minimum sum-of-squares clustering problems
Pattern Recognition
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The minimum sum-of-squares clustering problem is considered. The mathematical modeling of this problem leads to a min-sum-min formulation which, in addition to its intrinsic bi-level nature, has the significant characteristic of being strongly nondifferentiable. To overcome these difficulties, the resolution, method proposed adopts a smoothing strategy using a special C^~ differentiable class function. The final solution is obtained by solving a sequence of low dimension differentiable unconstrained optimization subproblems which gradually approach the original problem. The use of this technique, called hyperbolic smoothing, allows the main difficulties presented by the original problem to be overcome. A simplified algorithm containing only the essentials of the method is presented. For the purpose of illustrating both the reliability and the efficiency of the method, a set of computational experiments was performed, making use of traditional test problems described in the literature