Competitive Learning With Pairwise Constraints

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Provided by: Institute of Electrical & Electronic Engineers
Topic: Big Data
Format: PDF
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.
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