Amazon Data Mining Examples - Database Design - Lecture Slides, Slides of Database Management Systems (DBMS)

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Data Mining

Agenda

• Data Mining As Part of KDD
• Decision Tree
• Association Rules
• Clustering
• Amazon Data Mining Examples

What is Data Mining?

• “the automated extraction of hidden predictive information from large databases”
• Algorithms produce patterns, rules
• Predict future trends/behavior
• Used to make business decisions

Classification

• Items belong to classes
• Given past items’ classification, predict class of new item
• Example: Issuing credit cards
  • Use information: income, educational background, age, current debts
  • Credit worthiness: Bad, good, excellent

Credit Risk Decision Tree

Decision Tree Construction

• Some Definitions
  • Purity: > # instances of each leaf belonging to only 1 class means > purity
  • Best Split: split giving the maximum information gain ratio (info gain/info content) - Choose attribute and condition resulting in maximum purity

Association Rules

• antecedent  consequent
  • if  then
  • beer  diaper (Walmart)
  • economy bad  higher unemployment
  • Higher unemployment  higher unemployment benefits cost
• Rules associated with population, support, confidence

Association Rules

• Population: instances such as grocery store purchases
• Support
  • % of population satisfying antecedent and consequent
• Confidence
  • % consequent true when antecedent true

Clustering

• “The process of dividing a dataset into mutually exclusive groups such that the members of each group are as "close" as possible to one another, and different groups are as "far" as possible from one another, where distance is measured with respect to all available variables.”

Clustering

• Birch Algorithm
• points inserted into multidimensional tree
• items guided to leaf nodes "near" representative internal nodes
• nearby points clustered into one leaf node

Clustering

• 1. cluster people with similar movie preferences
• 2. given a new movie goer, find a cluster of similar movie goers
• 3. then predict the cluster's new movie preferences

Amazon Examples

Amazon Examples

Amazon Examples