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problem 1) describe data mining functionalities in detail.                         

problem 2) Describe in detail about Multi dimensional analysis and descriptive mining of complex data objects.       

problem 3)a) Describe how efficiency of Apriori can be improved                

b) How do you find frequent item sets using candidate generation?          

c) Describe how association rules are generated from frequent items.       

problem 4)a)i)  What are Bayesian classifiers.  Why is Naive Bayesian classifiers called “Naive”?                                  

ii)    Briefly describe Bayes Theorem.                    

b) What do you mean by Backpropagation?  describe  Backpropagation  for classification with algorithm.           

problem 5) prepare short notes on:

i)  K-Nearest Neighbour classifiers

ii) Case-based reasoning

iii) Genetic algorithm

iv) Rough set approach

v) Fuzzy set approaches                                          

problem 6)a) What do you mean by an outlier.                                

b) Describe the following approaches                      

i) Statistical –based outlier detection

ii) Distanced –based outlier detection

iii) Deviation –based outlier detection

problem 7)a) What is Regression Tree?                                           

b) prepare a detailed notes on:                               

i)   Intelligent miner

ii)  DB miner

iii) Enterprise miner                                

c) Describe the following data mining techniques             

i) Visual & Audio data mining

ii) Scientific & statistical data mining

problem 8) Describe the special features of BIRCH,  CURE, DBSCAN,  OPTICS.

DBMS, Programming

  • Category:- DBMS
  • Reference No.:- M97527

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