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MINING ALGORITHM - Coggle Diagram

- - - - Used in outlier detection applications such as detection of credit card fraud.
      - Helps in classifying documents on the web for information discovery.
      - Helps in identification of areas of similar land use in an earth observation database.
        
        Helps in the identification of groups of houses in a city according to house type, value, and geographic location.
      - Used to derive plant and animal taxonomies, categorize genes with similar functionalities and gain insight into structures inherent to populations.
      - Market research, pattern recognition, data analysis, and image processing.
        
        Help marketers discover distinct groups in their customer base
        
        Can characterize their customer groups based on the purchasing patterns.
  - - - Partitioning Method
        
        k-means, k-medoids, CLARANS
      - Hierarchical Method
        
        Diana, Agnes, BIRCH, CAMELEON
      - Density-based Method
        
        DBSACN, OPTICS, DenClue
      - Grid-Based Method
        
        STING, WaveCluster, CLIQUE
      - Model-Based Method
        
        Locates the clusters by clustering the density function. It reflects spatial distribution of the data points.
      - Constraint-based Method
        
        Performed by the incorporation of user or application-oriented constraints.
- - - - classification
        
        Cross-validation
        
        Hold-out Method
        
        Random Sub-sampling
        
        k-fold Cross-validation
        
        Leave-one-out Method
        
        Bootstrap
        
        Confusion Matrix
        
        Receiver Operating Curves (ROC)
      - association rule mining
        
        Support
        
        Confidence
        
        Lift
        
        Conviction
        
        Succinctness
      - clustering
        
        MDL Principle
- - - - USED IN MACHINE LEARNING AND PATTERN RECOGNITION
      - BUILD A MODEL THAT DESCRIBE IMPORTANT CLASS VARIABLE
    - - FOR PREDICTION OF NUMERIC ATTRIBUTES
      - PRODICT CONTINUOUS VALUES
  - - - ROOT NODE
        
        PRIMARY NODE IN THE PATH
        
        KNOT FROM WHICH ALL OTHER CHOICES, FORECASTS AND END NODES FINALLY DIVIDE
      - BRANCHES NODE
        
        BETWEEN ROOT AND LEAF NODES.
        
        REPRESENT CHANCE OR CONFUSION
      - LEAF NODE
        
        CONCLUSION OF THE DECISION ROUTE
  - - - HEALTH
      - TECHNOLOGIES
      - FINANCIAL PLANNING