title: Competitive learning with pairwise constraints.
abstract: Constrained clustering has been an active research topic since the last decade. Most studies focus on batch-mode algorithms. This brief introduces two algorithms for on-line constrained learning, named on-line linear constrained vector quantization error (O-LCVQE) and constrained rival penalized competitive learning (C-RPCL). The former is a variant of the LCVQE algorithm for on-line settings, whereas the latter is an adaptation of the (on-line) RPCL algorithm to deal with constrained clustering. The accuracy results--in terms of the normalized mutual information (NMI)--from experiments with nine datasets show that the partitions induced by O-LCVQE are competitive with those found by the (batch-mode) LCVQE. Compared with this formidable baseline algorithm, it is surprising that C-RPCL can provide better partitions (in terms of the NMI) for most of the datasets. Also, experiments on a large dataset show that on-line algorithms for constrained clustering can significantly reduce the computational time.
authors.forename: Thiago F
authors.initials: TF
authors.lastname: Covões
authors.forename: Eduardo R
authors.initials: ER
authors.lastname: Hruschka
authors.forename: Joydeep
authors.initials: J
authors.lastname: Ghosh
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007858:Learning; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated