Introduction of Data Mining and Association Rules

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Introduction of Data Mining and Association Rules

• cs157 Spring 2009
• Instructor Dr. Sin-Min Lee
• Student Dongyi Jia

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What is data mining?

• The automated extraction of hidden predictive
  information from database
• Allows users to analyze large databases to solve
  business decision problems.
• An extension of statistics, with a few artificial
  intelligence and machine learning twists thrown
  in.
• Attempts to discover rules and patterns from
  data.

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Data Mining - On What Kind of Data

• In principle, data mining should be applicable to
  any kind of information repositiory
• ? relational databases
• ? data warehouses
• ? transactional and advanced databases
• ? flat files
• ? World Wide Web

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Data Mining Functionalities-What kinds of
Patterns Can be Mined?

• Association Analysis
• Classification and Prediction
• Cluster Analysis
• Evolution Analysis

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Applications of data mining

• Require some sort of Prediction
• for example when a person applies for a
  credit card, the credit-card company wants to
  predict if the person is a good credit risk.
• Looks for Associations
• for example if a customer buys a book, an
  on-line bookstore may suggest other associated
  books.

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Associations Rule Discovery

• Task Discovering association rules among items
  in a transaction database.
• How are association rules mined from large
  database?
• 1. Find all frequent itemset each of these
  itemsets will occur at least as frequent as
  pre-determined minimum support count.
• 2. Generate strong association rules from the
  frequent itemsets these rules must satisfy
  minimum support and minimum confidence.

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Association Rules (cont.)

• Retail shops are often interested in associations
  between items that people buy.
• Someone who buys bread is quite likely also to
  buy milk.
• association rule bread gt milk
• A person who brought the book Database System
  Concepts is quite likely also to buy the book
  Operating System Concepts.
• association rule DSC gt OSC

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Association Rules (cont.)

• Two numbers
• Support is a measure of what fraction of the
  population satisfies both the antecedent and the
  consequent of the true.
• Confidence is a measure of how often the
  consequent is true when the antecedent is true.

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Association Rules (cont.)

• Let I i1, i2, im be a total set of items
• D is a set of transactions
• d is one transaction consists of a set
  of items
• d ? I
• Association rule
• X ? Y where X ? I ,Y ? I and X ? Y ?
• support (of transactions contain X ? Y ) /D
• confidence (of transactions contain X ? Y ) /
• of transactions contain X

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example

• Example of transaction data
• CD player, musics CD, musics book
• CD player, musics CD
• musics CD, musics book
• CD player
• I CD player, musics CD, musics book
• D 4
• of transactions contain both CD player, musics
  CD 2
• of transactions contain CD player 3
• CD player ? musics CD (sup2/4 , conf 2/3 )

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Association Rules (cont.)

• Rule support and confidence reflect the
  usefulness and certainty of discovered rules.
• A support of 50 for association rule means that
  50 of all the transactions under analysis that
  CDs player and music CD are purchased together.
• A confidence of 67 means that 67 of the
  customers who purchased a CDs player also bought
  music CD.

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Strong Association Rule

• User sets support and confidence thresholds.
• Rules above support threshold have LARGE support.
• Rules above confidence threshold have HIGH
  confidence.
• Rules satisfying both are said to be STRONG.

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References

• Professor Lees lectures
• http//www.cs.sjsu.edu/lee/cs157b/cs157b.html
• Rui Zhao, SJSU
• http//www.cs.sjsu.edu/lee/cs157b/cs157b.html
• Jiawei Han, Micheline Kamber
• Data Mining Concepts and Techniques
• Morgan Kaufmann Publishers

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Thank you !