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.