Bicriterion Cluster Analysis
From MaRDI portal
Publication:3908335
DOI10.1109/TPAMI.1980.4767027zbMath0458.62049WikidataQ84831962 ScholiaQ84831962MaRDI QIDQ3908335
Pierre Hansen, Michel Delattre
Publication date: 1980
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence (Search for Journal in Brave)
homogeneitydiameterseparationsplitdissimilaritybicriterion cluster analysiscoloration of graphefficient partition
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Graph theory (05C99)
Related Items
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, Cluster analysis and mathematical programming, Sequential clustering with radius and split criteria, Monotone linkage clustering and quasi-concave set functions, SingleCross-clustering: an algorithm for finding elongated clusters with automatic estimation of outliers and number of clusters, The weighted sum of split and diameter clustering, Visual attractiveness in vehicle routing via bi-objective optimization, Clustering on trees, Pattern classification problems and fuzzy sets, Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes, Graph theoretical clustering based on limited neighbourhood sets, A review of multiobjective programming and its application in quantitative psychology, Multiobjective blockmodeling for social network analysis, Clustering based on multiple paths, Efficient algorithms for divisive hierarchical clustering with the diameter criterion, Fuzzy clustering algorithms based on the maximum likelihood principle, Constrained clustering by constraint programming, Minimum sum of diameters clustering, A sampling-based exact algorithm for the solution of the minimax diameter clustering problem, Maximum sum-of-splits clustering, Order-constrained solutions in \(K\)-means clustering: even better than being globally optimal, The conditional \(p\)-dispersion problem, Computing sharp bounds for hard clustering problems on trees, A Lagrangian-based score for assessing the quality of pairwise constraints in semi-supervised clustering, Maximizing the ratio of cluster split to cluster diameter without and with cardinality constraints, Espaliers: A generalization of dendrograms, Hierarchical clustering based on ordinal consistency, Clustering heuristics for set covering, Set partition by globally optimized cluster seed points
