  k-nearest neighbour (kNN) is one of the most prominent, simple and basic
algorithm used in machine learning and data mining. However, kNN has limited
prediction ability, i.e., kNN cannot predict any instance correctly if it does
not belong to any of the predefined classes in the training data set. The
purpose of this paper is to suggest an Advanced kNN (A-kNN) algorithm that will
be able to classify an instance as unknown, after verifying that it does not
belong to any of the predefined classes. Performance of kNN and A-kNN is
compared on three different data sets namely iris plant data set, BUPA liver
disorder data set, and Alpha Beta detection data set. Results of A-kNN are
significantly accurate for detecting unknown instances.
