International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

Downloads: 103 | Views: 154

Research Paper | Information Technology | India | Volume 4 Issue 4, April 2015 | Rating: 6.6 / 10

Detection of Outliers Using Hybrid Algorithm on Categorical Datasets

Rachana P. Jakkulwar | Prof. R. A. Fadnavis

Abstract: The outlier is an observation that is different from the other remaining values in a data set. Real life contains large number of categorical data. There is some outlier detection algorithms have been designed for categorical data. There are two main problems of outlier detection for categorical data, which are the time complexity and accuracy for detection of outliers in categorical dataset. Categorical dataset have some limited approaches as compared to numeric dataset. This paper describes about some existing algorithms for outlier detection in categorical dataset. The novel Hybrid method which overcomes limitations of previous methods (NAVF and ROAD) has been implemented. The algorithm is implemented and tested on different types of networking datasets, in which detected outliers are virus or intrusion whose behavior is different than behavior in normal networking data.

Keywords: outliers, categorical data, hybrid approach, networking dataset, ranking and NAVF algorithm

Edition: Volume 4 Issue 4, April 2015,

Pages: 2734 - 2737

How to Download this Article?

Type Your Valid Email Address below to Receive the Article PDF Link

Verification Code will appear in 2 Seconds ... Wait