Course Outline

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

Requirements

Fair knowledge about relational data structures, SQL

 21 Hours

Testimonials (5)

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