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• Embedding: Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction ... ...find outliers
• AI Solver
  • Looking for event based?
    • Bidirectional Long Short-Term Memory (BI-LSTM) with Attention Mechanism
  • 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...
    • K-Means Clustering
    • Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)
    • One-class Support Vector Machine (SVM)
      • Variational Autoencoder (VAE)
    • Autoencoder (AE) / Encoder-Decoder
    • Radial Basis Function Network (RBFN)
• 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
  • Intruder
  • Cybersecurity
  • Signals
  • Pathology

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Outliers don't necessarily represent abnormal behavior

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An Outlier is a rare chance of occurrence within a given data set. In Data Science, an Outlier is an observation point that is distant from other observations. An Outlier may be due to variability in the measurement or it may indicate experimental error. Outliers, being the most extreme observations, may include the sample maximum or sample minimum, or both, depending on whether they are extremely high or low. However, the sample maximum and minimum are not always outliers because they may not be unusually far from other observations. - Outlier Detection and Anomaly Detection with Machine Learning | Mehul Ved - Medium