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A survey on outlier explanations.
Egawati Panjei1, Le Gruenwald1, Eleazar Leal2
1School of Computer Science, The University of Oklahoma, Norman, OK USA.
The VLDB Journal : Very Large Data Bases : a Publication of the VLDB Endowment
|January 31, 2022
Summary
This paper surveys outlier explanations, bridging the gap between detecting anomalies and understanding them. It details methods for generating and evaluating these explanations to aid user action.
Area of Science:
- Data Science
- Machine Learning
- Artificial Intelligence
Background:
- Outlier detection methods are prevalent, but interpreting detected outliers remains a challenge for users.
- Lack of clear explanations hinders timely and appropriate actions based on anomalous data.
- Existing literature lacks comprehensive surveys on outlier explanations.
Purpose of the Study:
- To provide the first comprehensive survey on outlier explanations.
- To define types of outlier explanations and discuss generation challenges.
- To review existing techniques, applications, and evaluation methods for outlier explanations.
Main Methods:
- Systematic literature review of outlier explanation techniques.
- Categorization of different types of outlier explanations.
- Analysis of challenges in generating and evaluating outlier explanations.
Main Results:
- Identified a gap in the literature regarding outlier explanations.
- Defined and categorized various types of outlier explanations.
- Reviewed existing techniques, applications, and evaluation metrics for outlier explanations.
Conclusions:
- Outlier explanations are crucial for actionable insights from anomalous data.
- This survey provides a foundation for understanding and advancing research in outlier explanations.
- Future research directions in outlier explanation generation and evaluation are discussed.
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