Data Mining -Data Mining: A KDD Process

1. Data Mining
Steps and Functionalities
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2. Data Mining: A KDD Process
 Data mining: the core of
knowledge discovery
process.
Data Cleaning
Data Integration
Databases
Data
Warehouse
Task-relevant Data
Selection &
Transformation
Data Mining
Pattern Evaluation
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3. Steps of a KDD Process
 Data Cleaning
 Handles Noisy, Inconsistent, Incomplete data
 Missing Values
 Noisy data
 Binning, Clustering etc.
 Inconsistencies
 Tools, functional dependencies
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4.  Data Integration
 Schema Integration
 Entity Identification problem
 Redundancy
 Correlation Analysis
 Data Selection
 Select Only the task relevant data
Steps of a KDD Process
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5.  Data Transformation
 Transform or consolidate data
 Smoothing, Normalization, Feature Construction
 Data Reduction - Compression
 Data Mining
 Intelligent methods are applied to extract patterns
Steps of a KDD Process
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6.  Pattern Evaluation
 Interestingness Measures
 Knowledge Presentation
 Visualization
Steps of a KDD Process
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7. Data Mining Functionalities
 Descriptive
 Characterize general properties of the data
 Predictive
 Performs inference
 Mining
 Parallel
 Various Granularities
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8. Data Mining Functionalities
 Concept/class description
 Association Analysis
 Classification and Prediction
 Cluster Analysis
 Outlier Analysis
 Evolution Analysis
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9. Concept/ Class Description
 Data can be associated with Classes /
Concepts
 Computers, Printers
 BigSpenders Vs BudgetSpenders
 Class / Concept Description
 Classes and Concepts can be summarized in
concise and precise terms
 Data Characterization
 Data Discrimination
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10. Data Characterization
 Summarization of the general characteristics
 Data collected and aggregated
 OLAP roll up operation
 Attribute Oriented Induction
 Results – Charts, cubes, rules
 Example
 Characteristics of Customers
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11. Data Discrimination
 Compare target class and contrasting classes
 Maybe user specified
 Examples:
 Products whose sales increased Vs decreased
 Regular Shoppers Vs Occasional Shoppers
 Output includes Comparative measures
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12. Association Analysis
 Discovery of association rules
 Form: X ⇒ Y
 Multi-dimensional
 Age(X, “20…29”) ∧ income(X, “20K…25K”) ⇒
buys(X, “Laptop”)
 Single Dimensional
 buys(X, “Laptop”) ⇒ buys(X, “Software”)
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13. Classification and Prediction
 Classification
 Finds models that describe and differentiate
classes or concepts
 Predicts class
 Training data
 Models – rules, decision trees, NN, formulae
 Preceded by relevance analysis (to eliminate
irrelevant attributes)
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14. Classification and Prediction
 Prediction
 Derived model is used for prediction
 Data value prediction
 Class label prediction (Classification)
 Trend identification
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15. Cluster Analysis
 Unsupervised
 Class labels are missing in the training set
 Maximize Intra-class similarity
 Minimize Inter-class similarity
 Hierarchy of classes
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16. Outlier Analysis
 Objects that do not comply with the general
behavior
 Noise Vs Rare events
 Fraud detection
 Statistical tests
 Deviation based methods
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17. Evolution Analysis
 Trend detection
 Time series data
 Involves other functionalities
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