Data Mining Lecture Notes

Introduction

• Analogy: Data mining compared to panning for gold
  • Requires sorting through vast amounts of data to extract valuable insights
• Definition: Process of extracting valuable information from large datasets
  • Used across various industries like marketing and healthcare
  • Helps businesses make informed decisions

Fundamentals of Data Mining

• Purpose: Process data to identify patterns and trends
• Evolution: Rapid advancements with the rise of data warehouses and big data
• Advantages:
  • Predict future trends by analyzing past data
  • Identify relationships between data pieces (e.g., website time and purchase likelihood)

Data Mining Process

1. Setting Objectives
   • Collaboration between data scientists and business stakeholders
   • Define business problems data mining will address
2. Data Preparation
   • Identify and clean the relevant dataset
   • Remove duplicates, missing values, and outliers
3. Applying Data Mining Algorithms
   • Look for interesting data relationships using algorithms
   • Employ deep learning techniques
4. Evaluating Results
   • Interpret valid, novel, useful, and understandable results

Data Mining Techniques

• Association
  • Rule-based method for finding variable relationships
  • Example: Correlating cream and strawberry purchases
• Classification
  • Identifies classes by describing multiple attributes
  • Example: Classifying cars by attributes like seats and shape
• Clustering
  • Groups data into structures based on similarities
• Deep Learning Techniques
  • Utilize artificial neural networks for making predictions
  • Methods like decision trees and K Nearest Neighbor (KNN)

Key Considerations

• No One-Size-Fits-All: Techniques vary in effectiveness based on data and business goals
• Trial and Error: Necessary to find the most effective method

Conclusion

• Data mining combines business stakeholders and data scientists
• Properly executed, it can lead to transformational insights
• Encouragement to engage with content: Questions, likes, and subscriptions