Data mining involves multiple steps in the knowledge discovery process including data cleaning, integration, selection, transformation, mining, and pattern evaluation. It has various functionalities including descriptive mining to characterize data, predictive mining for inference, and different mining techniques like classification, association analysis, clustering, and outlier analysis.

1. Data Mining
Steps and Functionalities
1

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
2

3. Steps of a KDD Process
 Data Cleaning
 Handles Noisy, Inconsistent, Incomplete data
 Missing Values
 Noisy data
 Binning, Clustering etc.
 Inconsistencies
 Tools, functional dependencies
3

4.  Data Integration
 Schema Integration
 Entity Identification problem
 Redundancy
 Correlation Analysis
 Data Selection
 Select Only the task relevant data
Steps of a KDD Process
4

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
5

6.  Pattern Evaluation
 Interestingness Measures
 Knowledge Presentation
 Visualization
Steps of a KDD Process
6

7. Data Mining Functionalities
 Descriptive
 Characterize general properties of the data
 Predictive
 Performs inference
 Mining
 Parallel
 Various Granularities
7

8. Data Mining Functionalities
 Concept/class description
 Association Analysis
 Classification and Prediction
 Cluster Analysis
 Outlier Analysis
 Evolution Analysis
8

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
9

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
10

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
11

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”)
12

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)
13

14. Classification and Prediction
 Prediction
 Derived model is used for prediction
 Data value prediction
 Class label prediction (Classification)
 Trend identification
14

15. Cluster Analysis
 Unsupervised
 Class labels are missing in the training set
 Maximize Intra-class similarity
 Minimize Inter-class similarity
 Hierarchy of classes
15

16. Outlier Analysis
 Objects that do not comply with the general
behavior
 Noise Vs Rare events
 Fraud detection
 Statistical tests
 Deviation based methods
16

17. Evolution Analysis
 Trend detection
 Time series data
 Involves other functionalities
17