Web Analytics Understanding user behavior and ... - pace university

With data mining, a retailer could use point-of-sale records of customer purchases to send targeted
promotions based on an individual's purchase history. By mining demographic data from comment
or warranty cards, the retailer could develop products and promotions to appeal to specific
customer segments.
For example, Blockbuster Entertainment mines its video rental history database to recommend
rentals to individual customers. American Express can suggest products to its cardholders based on
analysis of their monthly expenditures.
Learning Decision Trees
Decision tree induction is one of the simplest and yet most successful forms of learning algorithm.
A decision tree takes an input an object or situation described by a set of properties, and outputs a
yes/no "decision". Each internal node in the tree corresponds to a test of the value of one of the
properties, and the branches from the node are labeled with the possible values of the test. Each
leaf node in the tree specifies the Boolean value to be returned if that leaf is returned.
When to consider using decision trees
Decision trees are to be used when the instances are described by Attribute-Value Pairs. For
example when instances are described by a fixed set of attributes like (temperature) and values
(hot). Decision trees can be used when the training data is possibly noisy (in correct data: label
errors or attribute errors) and when the function is discrete valued.
For example: Decision trees are widely used in the equipment or medical diagnosis. Today
decision trees are widely used in risk analysis for credit, loans, insurance, consumer fraud,
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