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Outlier Detection using Reverse Neares Neighbor for Unsupervised Data

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Outlier Detection using Reverse Neares Neighbor for Unsupervised Data


V. V. R. Manoj | V. Aditya Rama Narayana | A. Bhargavi | A. Lakshmi Prasanna | Md. Aakhila Bhanu

https://doi.org/10.31142/ijtsrd11406



V. V. R. Manoj | V. Aditya Rama Narayana | A. Bhargavi | A. Lakshmi Prasanna | Md. Aakhila Bhanu "Outlier Detection using Reverse Neares Neighbor for Unsupervised Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3, April 2018, pp.1511-1513, URL: https://www.ijtsrd.com/papers/ijtsrd11406.pdf

Data mining has become one of the most popular and new technology that it has gained a lot of attention in the recent times and with the increase in the popularity and the usage there comes a lot of issues/problems with the usage one of it Outlier detection and maintaining the datasets without the expected patterns. To identify the difference between Outlier and normal behavior we use key assumption techniques. We Provide the reverse nearest neighbor technique. There is a connection between the hubs and antihubs, outliers and the present unsupervised detection methods. With the KNN method it will be possible to identify and influence the outlier and antihub methods on real life datasets and synthetic datasets. So, From this we provide the insight of the Reverse neighbor count on unsupervised outlier detection.

Reverse nearest neighbor; Outlier detection


IJTSRD11406
Volume-2 | Issue-3, April 2018
1511-1513
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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