  kNN is a very effective Instance based learning method, and it is easy to
implement. Due to heterogeneous nature of data, noises from different possible
sources are also widespread in nature especially in case of large-scale
databases. For noise elimination and effect of pseudo neighbours, in this
paper, we propose a new learning algorithm which performs the task of anomaly
detection and removal of pseudo neighbours from the dataset so as to provide
comparative better results. This algorithm also tries to minimize effect of
those neighbours which are distant. A concept of certainty measure is also
introduced for experimental results. The advantage of using concept of mutual
neighbours and distance-weighted voting is that, dataset will be refined after
removal of anomaly and weightage concept compels to take into account more
consideration of those neighbours, which are closer. Consequently, finally the
performance of proposed algorithm is calculated.
