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Chapter: Data Warehousing and Data Mining : Association Rule Mining and Classification

Rule Based Classification

Rule-based ordering (decision list): rules are organized into one long priority list, according to some measure of rule quality or by experts

Rule Based Classification

 

Using IF-THEN Rules for Classification

 

Represent the knowledge in the form of IF-THEN rules

 

R:  IF age = youth AND student = yes THEN buys_computer = yes

 

Rule antecedent/precondition vs. rule consequent

Assessment of a rule: coverage and accuracy

 

o   ncovers = # of tuples covered by R

o   ncorrect = # of tuples correctly classified by R

o   o      coverage(R) = ncovers /|D| /* D: training data set */

o   accuracy(R) = ncorrect / ncovers

If more than one rule is triggered, need conflict resolution

 

o   Size  ordering:  assign  the  highest  priority  to  the  triggering  rules  that  has  the

 

o   ―toughest‖ requirement (i.e., with the most attribute test)

 

o   Class-based ordering: decreasing order of prevalence or misclassification cost per class

 

o   Rule-based ordering (decision list): rules are organized into one long priority list, according to some measure of rule quality or by experts

 

Rule Extraction from a Decision Tree

 

o   Rules are easier to understand than large trees

 

o   One rule is created for each path from the root to a leaf

 

o   Each attribute-value pair along a path forms a conjunction: the leaf holds the class prediction

 

o   Rules are mutually exclusive and exhaustive

 


Example: Rule extraction from our buys_computer decision-tree


 

Rule Extraction from the Training Data

 

o     Sequential covering algorithm: Extracts rules directly from training data

 

o     Typical sequential covering algorithms: FOIL, AQ, CN2, RIPPER

 

o     Rules are learned sequentially, each for a given class Ci will cover many tuples of Ci but none (or few) of the tuples of other classes

 

o     Steps:

·        Rules are learned one at a time

·        Each time a rule is learned, the tuples covered by the rules are removed

·        The process repeats on the remaining tuples unless termination condition, e.g., when no more training examples or when the quality of a rule returned is below a user-specified threshold

 

o     Comp. w. decision-tree induction: learning a set of rules simultaneously


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