Data Mining - Bayesian classification, undefined for Data Mining. Moradabad Institute of Technology (MIT)

Data Mining

Description: Summary about Classification and Prediction, Bayesian Theorem: Basics, Bayesian Theorem, Towards Naïve Bayesian Classifier, Naïve Bayesian Classifier: Training Dataset, Avoiding the 0-Probability Problem.
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,-
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0!"
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4!"
#
0'
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A statistical classifier: performs probabilistic prediction, i.e., predicts
class membership probabilities( that a given tuple belongs to a particular
class)
Foundation: Based on Bayes’ Theorem given by Thomas Bayes
Performance: A simple Bayesian classifier, naïve Bayesian classifier,
has comparable performance with decision tree and selected neural
network classifiers.
Class Conditional Independence : Naïve Bayesian Classifiers assume
that the effect of an attribute value on a given class is independent of the
values of the other attributes. This assumption is called class conditional
independence.
Incremental: Each training example can incrementally increase/decrease
the probability that a hypothesis is correct prior knowledge can be
combined with observed data
Standard: Even when Bayesian methods are computationally intractable,
they can provide a standard of optimal decision making against which
other methods can be measured
Bayesian Belief Network: are graphical models that allow the
representation of dependencies among subsets of attributes

 6
('(
1X!.7evidence8/!!!+9
1HhypothesisX!!
5  49!3!"'
 :6;'!9<
!"    P(H|X),  !'   '
!!X. #.=>:/?!'
X9!!'9+9@

P(H).prior probability/!!'
5   X9!!'!3A
P(X):!'3X!'!.
336;'!<
P(X|H) .posteriori probability/  !' 3   ! X
'!
5   B X9!!' :6 
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('
B   X, posteriori probability of a
hypothesis =, #.=>X/, 3!!9('
&3!!'9
C!+!DE
# X !   F  !' #.>X/ 
!!#.+>:/3!!k!
#!G!'!+9!3'
!"!
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University: Moradabad Institute of Technology (MIT)
Subject: Data Mining
Upload date: 03/09/2011
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