Difference between revisions of "...find outliers"
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* [http://medium.com/@srnghn/machine-learning-trying-to-detect-outliers-or-unusual-behavior-2d9f364334f9 Machine Learning: Trying to detect outliers or unusual behavior | Stacey Ronaghan - Medium] | * [http://medium.com/@srnghn/machine-learning-trying-to-detect-outliers-or-unusual-behavior-2d9f364334f9 Machine Learning: Trying to detect outliers or unusual behavior | Stacey Ronaghan - Medium] | ||
* [http://pdfs.semanticscholar.org/4c68/4a9ba057fb7e61733ff554fe2975a2c91096.pdf Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering | A. Barai and L. Dey] | * [http://pdfs.semanticscholar.org/4c68/4a9ba057fb7e61733ff554fe2975a2c91096.pdf Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering | A. Barai and L. Dey] | ||
| − | * [[Capabilities]] | + | * [[Capabilities]] ... [[AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]] |
** [[Intruder]] | ** [[Intruder]] | ||
** [[Cybersecurity]] | ** [[Cybersecurity]] | ||
Revision as of 14:13, 20 March 2023
- AI Solver
- Looking for event based?
- Do you have > 100 features?
- Yes, then try One-class Support Vector Machine (SVM)
- No, need fast training, then try Principal Component Analysis (PCA)-based Anomaly Detection
- Also consider...
- Outlier | Wikipedia
- Machine Learning: Trying to detect outliers or unusual behavior | Stacey Ronaghan - Medium
- Outlier Detection and Removal Algorithm in K-Means and Hierarchical Clustering | A. Barai and L. Dey
- Capabilities ... Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction ... ...find outliers
Anomaly Detection. Sometimes the goal is to identify data points that are simply unusual. In fraud detection, for example, any highly unusual credit card spending patterns are suspect. The possible variations are so numerous and the training examples so few, that it's not feasible to learn what fraudulent activity looks like. The approach that anomaly detection takes is to simply learn what normal activity looks like (using a history non-fraudulent transactions) and identify anything that is significantly different.
