Information Retrieval
Similarity of personal preferences: theoretical foundations and empirical analysis
Artificial Intelligence
How slow is the k-means method?
Proceedings of the twenty-second annual symposium on Computational geometry
Clustering constrained symbolic data
Pattern Recognition Letters
Smoothed Analysis of the k-Means Method
Journal of the ACM (JACM)
Partitioning hard clustering algorithms based on multiple dissimilarity matrices
Pattern Recognition
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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We present a new algorithm capable of partitioning sets of objects by taking simultaneously into account their relational descriptions given by multiple dissimilarity matrices. The novelty of the algorithm is that it is based on a nonlinear aggregation criterion, weighted Tchebycheff distances, more appropriate than linear combinations (such as weighted averages) for the construction of compromise solutions. We obtain a hard partition of the set of objects, the prototype of each cluster and a weight vector that indicates the relevance of each matrix in each cluster. Since this is a clustering algorithm for relational data, it is compatible with any distance function used to measure the dissimilarity between objects. Results obtained in experiments with data sets (synthetic and real) show the usefulness of the proposed algorithm.