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Data Mining - Coggle Diagram

- - - - Distributive
        
        count(), sum(), min(), max()
      - Algebraic
        
        avg(), std()
      - Holistic
        
        median(), mode(), rank()
  - - - Data source view
      - Data warehouse views
      - Business Query View
      - Views
      - Top down
    - - Business process to model
      - Atomic Level of data - Grain
      - Choose the Dimensions
      - Choose the measure
    - - Information processing
      - Analytical Processing
      - Data Mining
- - - - Normalize function
        
        std
        
        min-max
        
        etc...
      - Euclidean distance
  - - - \( \frac {1} {N} \sum_{i=1}^N \mathbb{I} [f(x_i) = y_i] \)
    - - TP
      - FP
      - TN
      - FN
      - precision
        
        \( \frac {N_{TP}} {N_{TP} + N_{FP}} \)
      - recall
        
        \( \frac {N_{TP}} {N_{TP} + N_{FN}} \)
      - \(F_1 \) Score
        \( \frac{2 \times \text{precision} \times \text{recall}}{\text{precision} + \text{recall}} \)
      - Precision - Recall Curve
      - Confusion Matrix
  - - - Random Forrest
- - - - Assign
        
        Move
        
        Assign
        
        .... and so on
        
        Final
        
        .
    - - Euclidean distance
        \( e(x, x') = ||x - x'|| = \sqrt{\sum_{i=1}^n (x_i - x_i')^2} \)
- - - - Boxplots
      - Grubb's Test
        
        \( G > \frac{N-1}{\sqrt{N}} \sqrt{\frac{t_{\alpha/(2N),N-2}^2}{N - 2 + t_{\alpha/(2N),N-2}^2}} \)
      - Mahalanobis distance
        
        \(MDist(x, \bar{x}) = (x − \bar{x})^TS-1(x − \bar{x})\)
    - - Outlier detection using histograms
    - - Local outliers
    - - Object does not belong to any cluster
        
        DBSCAN algorithm
      - There is a large distance between the object and its closest
        cluster.
      - Object belongs to a small or sparse cluster
        
        FindCBLOF Algorithm