Data Mining Treningskurs

Introduction

• Data mining as the analysis step of the KDD process ("Knowledge Discovery in Databases")
• Subfield of computer science
• Discovering patterns in large data sets

Sources of methods

• Artificial intelligence
• Machine learning
• Statistics
• Database systems

What is involved?

• Database and data management aspects
• Data pre-processing
• Model and inference considerations
• Interestingness metrics
• Complexity considerations
• Post-processing of discovered structures
• Visualization
• Online updating

Data mining main tasks

• Automatic or semi-automatic analysis of large quantities of data
• Extracting previously unknown interesting patterns
  • groups of data records (cluster analysis)
  • unusual records (anomaly detection)
  • dependencies (association rule mining)

Data mining

• Anomaly detection (Outlier/change/deviation detection)
• Association rule learning (Dependency modeling)
• Clustering
• Classification
• Regression
• Summarization

Use and applications

• Able Danger
• Behavioral analytics
• Business analytics
• Cross Industry Standard Process for Data Mining
• Customer analytics
• Data mining in agriculture
• Data mining in meteorology
• Educational data mining
• Human genetic clustering
• Inference attack
• Java Data Mining
• Open-source intelligence
• Path analysis (computing)
• Reactive business intelligence

Data dredging, data fishing, data snooping