README.md

In p-jayanna/gbod: Outlier Detection

gbod

Grid Based Outlier Detection

Outlier detection is one of the most widely used technique to identify abnormal behavior in raw data. The sense of abnormal deviation mentioned here accounts for human or system errors that naturally occur as part of the data. In this paper, we propose a new algorithm called Grid Based Outlier Detection (GBOD) to find the hidden outliers in large data sets. In contrast to existing grid based methods which are limited to only some statistical based approaches, the GBOD algorithm is raised with two alternations to figure out different range of outliers depending upon the interest of the user. First, number of points in local grid is used to decide whether a point is an outlier or not. Whereas, the second part is an approach to give an outlier score for each points in case of the outlier score is important for the decision making. The simple structure makes this algorithm extremely effective for large data sets, while existing algorithms weaken in this challenge.

p-jayanna/gbod documentation built on May 5, 2019, 9:02 p.m.