Outlier Detection using Reverse Neares Neighbor for Unsupervised Data IJTSRD
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.
By 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,
URL: http://www.ijtsrd.com/papers/ijtsrd11406.pdf
call for paper Parallel Computing, international journal World Wide Web, science journal