Data Mining as a Step in the Process of Knowledge Discovery in Databases (KDD).

 1. Data Cleaning

• Objective: Remove noise and inconsistent data.
• Action: Erase errors, handle missing values, and smooth out discrepancies from the raw datasets.

2. Data Integration

• Objective: Combine multiple heterogeneous data sources.
• Action: Merge databases, data cubes, or flat files into a unified repository.

3. Data Selection

• Objective: Isolate target data.
• Action: Retrieve only the data relevant to the specific analysis task from the integrated database.

4. Data Transformation

• Objective: Consolidate data into appropriate formats.
• Action: Convert and aggregate data into mining-ready structures through operations like summary or normalization.

5. Data Mining

• Objective: Extract data patterns.
• Action: Apply intelligent statistical and algorithmic methods to uncover hidden trends or relationships.

6. Pattern Evaluation

• Objective: Identify truly valuable insights.
• Action: Evaluate discovered patterns against predefined interestingness measures to separate trivial findings from actionable knowledge.

7. Knowledge Presentation

• Objective: Deliver insights to decision-makers.
• Action: Use visualization tools and knowledge representation techniques to clearly communicate the final results to users.

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