Data Mining and Decision Tree

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Data Miningand Decision Tree

• CS157B Spring 2006
• Masumi Shimoda

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Outline

• Brief introduction to data mining
• Definition
• Objective
• Application
• Decision tree

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What is Data Mining?

• Process of automatically finding the
  relationships and patterns, and extracting the
  meaning of enormous amount of data.
• Also called knowledge discovery

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Objective

• Extracting the hidden, or not easily recognizable
  knowledge out of the large data Know the past
• Predicting what is likely to happen if a
  particular type of event occurs Predict the
  future

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Application

• Marketing example
• Sending direct mail to randomly chosen people
• Database of recipients attribute data (e.g.
  gender, marital status, of children, etc) is
  available
• How can this company increase the response rate
  of direct mail?

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Application (Contd)

• Figure out the pattern, relationship of
  attributes that those who responded has in common
• Helps making decision of what kind of group of
  people the company should target

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• Data mining helps analyzing large amount of data,
  and making decisionbut how exactly does it work?
• One method that is commonly used is decision tree

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Decision Tree

• One of many methods to perform data mining -
  particularly classification
• Divides the dataset into multiple groups by
  evaluating attributes
• Decision tree can be explained a series of nested
  if-then-else statements.

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Decision Tree (Contd)

• Each non-leaf node has a predicate associated,
  testing an attribute of data
• Leaf node represents a class, or category
• To classify a data, start from root node and
  traverse down the tree by testing predicates and
  taking branches

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Example of Decision Tree
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Advantages of Decision Tree

• Easy to visualize the process of classification
• Can easily tell why the data is classified in a
  particular category - just trace the path to get
  to the leaf and it explains the reason
• Simple, fast processing
• Once the tree is made, just traverse down the
  tree to classify the data

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Decision Tree is for

• Classifying the dataset which
• The predicates return discrete values
• Does not have an attributes that all data has the
  same value