Data Mining Course Summary 2025
1. Introduction to Data Mining
• Definition: Data Mining is the process of discovering useful patterns,
trends, and knowledge from large datasets using techniques from statistics,
machine learning, and database systems.
• Purpose: Extract meaningful insights to support decision-making,
prediction, and understanding data.
• Difference from related fields:
o Data Mining vs. Machine Learning: Data mining focuses more on
knowledge discovery, often exploratory and descriptive, whereas ML
focuses more on predictive modeling.
o Data Mining vs. Database Systems: Data mining extracts patterns;
databases store and manage data.
2. Data Mining Process (Knowledge Discovery in Databases - KDD)
• Steps:
1. Data Cleaning: Remove noise, handle missing values.
2. Data Integration: Combine data from multiple sources.
3. Data Selection: Choose relevant data for analysis.
4. Data Transformation: Convert data into suitable format
(normalization, aggregation).
5. Data Mining: Apply algorithms to extract patterns.
