On-line learning is a training paradigm that allows the processing of constant data flows, so that learning adapts to new knowledge. However, due to the nature of the study problem, it is possible that in the clustering obtained there are data complexities (outliers, atypical patterns, noisy, etc.) that deteriorate the performance of the model in the classification stage. Due to the above, an alternative to cope data complexities is the use of algorithms that allow to detect reject options to filter noisy pattern. In this research the neighborhood-based reject option is implemented in an on-line learning process, with the intention of improving the clustering quality and thus increasing the precision indexes obtained with the nearest neighbor's rule in the classification stage. Likewise, to validate the quality of the clustering generated, internal and external analysis metrics are used. The experimental results show the viability of the proposal when analyzed on real data.
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