AnomalyDetection | Anomaly detection using python
kandi X-RAY | AnomalyDetection Summary
kandi X-RAY | AnomalyDetection Summary
AnomalyDetection is a Jupyter Notebook library. AnomalyDetection has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.
Anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions. In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns.
Anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions. In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns.
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Support
AnomalyDetection has a low active ecosystem.
It has 0 star(s) with 0 fork(s). There are no watchers for this library.
It had no major release in the last 6 months.
AnomalyDetection has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of AnomalyDetection is current.
Quality
AnomalyDetection has no bugs reported.
Security
AnomalyDetection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
AnomalyDetection does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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AnomalyDetection releases are not available. You will need to build from source code and install.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of AnomalyDetection
AnomalyDetection Key Features
No Key Features are available at this moment for AnomalyDetection.
AnomalyDetection Examples and Code Snippets
No Code Snippets are available at this moment for AnomalyDetection.
Community Discussions
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Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install AnomalyDetection
You can download it from GitHub.
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If you have any questions check and ask questions on community page Stack Overflow .
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