An outlier is an observation, such as a data point, that exists a significant distance from most other observations.
Outliers sometimes occur due to measurement errors. Other times, outliers can be indicators of important occurrences or events.
One such example is fraud detection, where outliers may indicate fraudulent activity.
As such, outlier detection and analysis is an interesting and challenging data mining task.
There are many different methods to detect outliers. In this Custom Visual, we implement five very popular detection methods:
1. Zscore is a standard deviation approach. For more details see: https://en.wikipedia.org/wiki/Standard_score
2. Tukey’s method, which uses interquartile (IQR) range approach. For more details see: http://datapigtechnologies.com/blog/index.php/highlighting-outliers-in-your-data-with-the-tukey-method/
3. LOF (Local Outlier Factor) method, which computes the local density deviation of a given data point with respect to its neighbors. It considers samples that have a substantially lower density than their neighbors as outliers. For more details see: https://en.wikipedia.org/wiki/Local_outlier_factor
4. Cook's distance, which shows the influence of each observation on the fitted response values. An observation with Cook's distance larger than three times the mean Cook's distance might be an outlier. For more details see: https://en.wikipedia.org/wiki/Cook%27s_distance
5. Finally, the manual method allows you to define your own upper and lower thresholds for the original data.
In addition to those methods, we support three ways to visualize outliers:
* Scatter plot
* Box plot
* Density plot
For each plot type, you can decide to visualize the original data, or outlier scores.
NEW: support for tooltips on hover and selection.
Here is how it works:
R package dependencies (which are auto-installed): scales, reshape, ggplot2, plotly, htmlwidgets, XML, DMwR
Supports R versions: R 3.4.1, R 3.3.3, R 3.3.2, MRO 3.2.2
This is an open source visual. Get the code from GitHub: https://github.com/Microsoft/powerbi-visuals-outliers-det