Data mining is a non-trivial process aimed at extracting valid and useful patterns from large datasets. The document outlines a data mining process that includes data cleaning, integration, selection, and pattern evaluation as part of knowledge discovery. It emphasizes the iterative nature of the process and the need for prior knowledge and effective algorithms.

1. Data Mining
By:
Rizgar Ramadhan

2. Data Mining as the Evolution of Information
Technology

3. What Is Data Mining?
 Data mining:
is non-trivial process of extraction valid,
novel,
potentially useful, and ultimately
understandable
patterns or information in data
 Alternative name

4. Process of Data Mining
Data Mining is essentially a process of
data extracting of not obvious but useful
information from large database.
The entire process interactive and iterative.

5. Process of Data Mining:
◦ Data mining—core of
knowledge discovery
process
Data Cleaning
Data Integration
Databases
Data Warehouse
Task-relevant Data
Selection
Data Mining
Pattern Evaluation

6. Steps of a KDD Process
 Learning the application domain
◦ Relevant prior knowledge and goals of application
 Creating a target data set: data selection
 Data cleaning and preprocessing: (may take 60% of
effort!)
 Data reduction and transformation
◦ Find useful features, dimensionality/variable reduction.
 Choosing functions of data mining
◦ Classification, regression, association, clustering.
 Choosing the mining algorithm(s)
 Pattern evaluation and knowledge presentation
◦ Visualization, transformation, removing redundant patterns, etc.
 Use of discovered knowledge