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Related Concept Videos

Outliers and Influential Points01:08

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
Anomaly analysisOutlier descriptionOutlier detectionOutlier explanationOutlier interpretation

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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.