• Blessa Binolin Pepsi M
• Deepika J
• Nandhini R
• Viruthika M

K-nearest neighbor (kNN) search is a significant issue in knowledge discovery and data mining. We suggest a novel approach for kNN search called random projection forests, which is motivated by the enormous success of tree-based techniques and ensemble approaches over the past decades (rpForests). In order to locate the closest neighbor’s rpForests combines several kNN-sensitive trees, with each through a sequence of arbitrary projections in recursive fashion. Experiments on a large number of real datasets have shown that our strategy is effective. Produces a remarkable accuracy in terms of the disparity in the kth nearest neighbor and the fast-declining missing rate of kNNs. As a tree-based approach, rpForests has a very low computational complexity. The rpForests ensemble structure makes on clustered or multicore machines, is readily parallelized to run, the running. By demonstrating the exponential decline of nearby points as a theoretical insight into IsolationForests when the ensemble size grows, they are divided by ensemble random projection trees.

Component,formatting,style,styling,insert. RpForests, K-Nearest Neighbor, ensemble, IsolationForests

"Application of K-nearest Neighbor in Medicine by Isolation Forest Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.56-64, June-2023, Available :http://www.jetir.org/papers/JETIRFZ06010.pdf

"Application of K-nearest Neighbor in Medicine by Isolation Forest Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. pp56-64, June-2023, Available at : http://www.jetir.org/papers/JETIRFZ06010.pdf