The fundamental operations in data mining are classification, cluster analysis, and outlier detection, and this might be most widely-encountered is that of similarity search. Similarity search is that the foundation of k-nearest-neighbor (k-NN) classification, which frequently produces competitively low error rates in practice, significantly once the quantity of categories is massive. In this paper we try to introduce a data structure for k-NN search, the Rank Cover Tree (RCT). The pruning tests for RCT rely on the comparison of similarity values not on the other properties of the underlying space, such as the triangle inequality. Objects are selected according to their ranks with respect to the query object, allowing much tighter control on the overall execution costs. Theoretical analysis shows that with very high probability, the RCT returns a correct query result in time that depends very competitively on a measure of the intrinsic dimensionality of the data set. The experimental results for the RCT show that non-metric pruning strategies for similarity search can be practical even when the representational dimension of the data is extremely high. They also show that the RCT is capable of meeting or exceeding the level of performance of state-of-the-art methods that make use of metric pruning or other selection tests involving numerical constraints on distance values. In this project the system proposed a probabilistic data structure for k-NN search, the rank cover tree (RCT) that entirely avoids the use of constraints involving similarity values.