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Data Anomaly Detection
course content

Kursinnhold

Data Anomaly Detection

Data Anomaly Detection

1. What is Anomaly Detection?
General InformationTypes of AnomaliesHow Outliers Influence On Prediction ResultsWhat Should We Do With Detected Outliers
2. Statistical Methods in Anomaly Detection
Rule-based ApproachChallenge: Rule-based Approach1.5 IQR Rule3-Sigma RuleMedian Absolute DeviationChallenge: Outlier Detection Using MAD Rule
3. Machine Learning Techniques
ClusteringChallenge: Using DBSCAN Clustering to Detect OutliersRegularisationChallenge: Solving Task Using RegularisationAutoencoders

book
Types of Anomalies

In this course, we will concentrate primarily on the first type of anomalies: point anomalies, also commonly referred to as outliers.

Characteristics of Point Anomalies:

  1. Extreme Values: Point anomalies are typically characterized by data points that have values significantly higher or lower than the values of the majority of data points in the dataset. These extreme values can disrupt the analysis and modeling processes;

  2. Isolation: Point anomalies are often isolated from the rest of the data. They do not follow the general patterns or trends observed in the majority of the data, making them stand out;

  3. Causes: Point anomalies can result from a variety of causes, including data entry errors, sensor malfunctions, equipment failures, rare and unexpected events, or even fraudulent activities.

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What are point anomalies in data anomaly detection?

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course content

Kursinnhold

Data Anomaly Detection

Data Anomaly Detection

1. What is Anomaly Detection?
General InformationTypes of AnomaliesHow Outliers Influence On Prediction ResultsWhat Should We Do With Detected Outliers
2. Statistical Methods in Anomaly Detection
Rule-based ApproachChallenge: Rule-based Approach1.5 IQR Rule3-Sigma RuleMedian Absolute DeviationChallenge: Outlier Detection Using MAD Rule
3. Machine Learning Techniques
ClusteringChallenge: Using DBSCAN Clustering to Detect OutliersRegularisationChallenge: Solving Task Using RegularisationAutoencoders

book
Types of Anomalies

In this course, we will concentrate primarily on the first type of anomalies: point anomalies, also commonly referred to as outliers.

Characteristics of Point Anomalies:

  1. Extreme Values: Point anomalies are typically characterized by data points that have values significantly higher or lower than the values of the majority of data points in the dataset. These extreme values can disrupt the analysis and modeling processes;

  2. Isolation: Point anomalies are often isolated from the rest of the data. They do not follow the general patterns or trends observed in the majority of the data, making them stand out;

  3. Causes: Point anomalies can result from a variety of causes, including data entry errors, sensor malfunctions, equipment failures, rare and unexpected events, or even fraudulent activities.

question mark

What are point anomalies in data anomaly detection?

Select the correct answer

Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 1. Kapittel 2
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