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Data Mining

  • Date Submitted: 07/28/2011 03:09 PM
  • Flesch-Kincaid Score: 39.1 
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Data Mining - An Introduction

Student Notes

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1 - Data mining
      1.1 - What is data mining?
      1.2 - Data mining background
          1.2.1 - Inductive learning
          1.2.2 - Statistics
          1.2.3 - Machine Learning
          1.2.4 - Differences between Data Mining and Machine Learning
      1.3 - Data Mining Models
          1.3.1 - Verification Model
          1.3.2 - Discovery Model
      1.4 - Data Warehousing
          1.4.1 - Characteristics of a data warehouse
          1.4.2 - Processes in data warehousing
          1.4.3 - Data warehousing and OLTP systems
          1.4.4 - The Data Warehouse model
          1.4.5 - Problems with data warehousing
          1.4.6 - Criteria for a data warehouse
      1.5 - Data mining problems/issues
          1.5.1 - Limited Information
          1.5.2 - Noise and missing values
          1.5.3 - Uncertainty
          1.5.4 - Size, updates, and irrelevant fields
      1.6 - Potential Applications
          1.6.1 - Retail/Marketing
          1.6.2 - Banking
          1.6.3 - Insurance and Health Care
          1.6.4 - Transportation
          1.6.5 - Medicine
2 - Data Mining Functions
      2.1 - Classification
      2.2 - Associations
      2.3 - Sequential/Temporal patterns
      2.4 - Clustering/Segmentation
          2.4.1 - IBM - Market Basket Analysis example
3 - Data Mining Techniques
      3.1 - Cluster Analysis
      3.2 - Induction
          3.2.1 - decision trees
          3.2.2 - rule induction
      3.3 - Neural networks
      3.4 - On-line Analytical processing
          3.4.1 - OLAP Example
          3.4.2 - Comparison of OLAP and OLTP
      3.5 - Data Visualisation
4 - Siftware - past and present developments
      4.1 - New architectures
          4.1.1 - Obstacles
          4.1.2 - The key
          4.1.3 - Oracle was first
          4.1.4 - Red Brick has a strong showing
          4.1.5 - IBM is...

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