DWDM questions

Unit 5:

1.a)write apriori algorithm and explain the same.
b)define the terms border set and frequent item set.

2.a)Explain market-basket analysis and its relevance to association rule.
b)find all frequent item sets for the foll. data using apriori algorithm with support count of 50%

tid list of items ids
t100 k,a,d,b
t200 d,a,c,e,b
t300 c,a,b,e
t400 b,a,d
t500 c,b,k

3.a) what are the applications of apriori property.explain join and prune actions.
b) gve one example for generating association rules from frequent item sets.

unit 6:

1.a) distinguish classification and prediction.
b) describe an algorithm like ID3 for constructing a decision tree.

2.a) compare the advantages and disadvantages of eager classification(decision trees) Vs Lazy classification(k-nearest neighbour).

3.a) differentiate between classification and clustering.
b) what is back propagation and how is it used for classification.

Unit 7:

1.a) explain about different datatypes used in cluster analysis.
b) discuss k-means algorithm.

2.a) write about typical requirements of clustering methods for their usage in data mining.
b) write about the categories of major clustering methods.

3. briefly outline how to compute the dissimilarity between objects described by the following types of variables.
a) Interval-scaled variables.
b)Binary variables.