An Evolutionary Approach to Multiobjective Clustering

By Julia Handl, Joshua D. Knowles. IEEE Transactions on Evolutionary Computation, 2007

671 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Food Science

View Paper: https://www.semanticscholar.org/paper/02f8537ffac71525c33b774dca511c1a739bb26c

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Citations in the corpus

Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction (2011, background): “Minimizing the intra-cluster distance and maximizing inter-cluster distance simultaneously in a bi-objective formulation of a clustering problem is found to yield better solutions than the usual singleobjective minimization of the ratio of the intra-cluster distance to the inter-cluster distance [43”
Ant-based and swarm-based clustering (2007, methodology): “It is even possible to consider several objectives simultaneously, an approach explored recently in (Handl and Knowles 2007).”
GA-Net: A Genetic Algorithm for Community Detection in Social Networks (2008, methodology): “Our clustering algorithm uses the locus-based adjacency representation proposed in [19] and employed by [9,14] for multiobjective clustering.”
Multiobjective Optimization in Bioinformatics and Computational Biology (2007, background): “This approach has been investigated for several unsupervised classification tasks [62], [68], but remains to be explored in many other application domains.”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, methodology): “[197] J. Handl, J. Knowles, M. Dorigo, Ant-Based Clustering: a Comparative Study of its Relative Performance with Respect to k-means, Average Link and 1DSOM, Technical Report TR/IRIDIA/2003-24, IRIDIA, University Libre de Bruxelles, Belgium, 2003.”
A Multiobjective Genetic Algorithm to Find Communities in Complex Networks (2012, methodology): “It is worth noting that the Pareto-optimal solutions as outlined in [28] always include the optimal solutions of the clustering problems with a single objective to optimize.”
Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, background): “…2000), and we shall largely ignore it, although we would point out that the field of semi-supervised learning is designed to help detect this and reclassify individuals as appropriate (Bennett and Demiriz, 1998; Demiriz et al., 1999; Kemp et al., 2003; Li et al., 2003; Handl and Knowles, 2006b).”
A review of clustering techniques and developments (2017, methodology): “In multi-objective clustering with automatic k-determination (MOCK) [78, 79], compactness of clusters is maximized as the first objective while the connectivity of the clusters is maximized as the second objective.”