Data Mining — Summer 2018

Explain following terms. 1) Data discrimination 2) Data cube 3) Roll-up operation 4) Neural Network 5) Outliers 6) Web mining 7) Define full forms : DIANA, AGNES

Differentiate between 1) Supervised learning and unsupervised learning 2) Eager learner classification and lazy learner classification

What is data mining? Describe the steps involved in data mining when viewed as a process of knowledge discovery.

Explain the various data pre-processing methods in brief.

Discuss Frequent Pattern Mining: A Road Map.

What is Apriori property? How the Apriori property is used in finding frequent itemset. Explain Join and Prune step.

Describe Decision Tree Induction algorithm. You can describe it with the help of an example.

Explain the terms “Tree Induction” and “Tree Pruning” in detail.

Discuss following in brief: 1) Information Gain 2) Gain Ratio 3) Gini Index

Explain the following classification methods in brief. 1) k-Nearest-Neighbor classifier 2) Case Based Reasoning classifier.

Write down the k-mediod algorithm for clustering and explain its working. How it is better than k-mean clustering algorithm?

What do you mean by cluster analysis? What are the requirements of cluster analysis?

List all categorization of major clustering methods and explain any three clustering methods.

Explain about mining complex data types.

Discuss Data Mining for Biological Data Analysis with DNA sequence and corresponding amino acid sequence.

Explain how data mining application is useful for Telecommunication Industry.

Discussion the application of data mining in Retail and Marketing.