Data Mining — Summer 2024

Define data mining. Describe three challenges to data mining regarding data mining methodology and user interaction issues.

Explain the steps in knowledge discovery.

Explain the various data mining issues.

What are the smoothing techniques available to remove noise?

Discuss normalization in detail.

In real-world data, tuples with missing values for some attributes are a common occurrence. Describe various methods for handling this problem.

Discuss data discretization and concept hierarchy generation.

How are association rules mined from large databases?

Give the difference between Boolean association rule and quantitative association rule.

What are the limitations of the apriori approach for mining? Briefly describe the techniques to improve the efficiency of apriori algorithm.

Describe two interesting measures for association rules.

How Meta rules are useful in constraint based association mining.

Write an algorithm for finding frequent item-sets using candidate generation.

What are the difference between supervised learning and unsupervised learning?

Write down short note on backpropagation.

What is information gain? Explain the steps required to generate a decision tree from a training data set.

Differentiate between linear regression and nonlinear regression.

Explain various methods of evaluating accuracy of classifier.

Write a short on: web content mining.

Explain temporal mining.

Differentiate between partitioning and hierarchical methods for clustering.

Explain following clustering algorithm in details: 1) CLARA 2) BIRCH

List out the applications of distributed and parallel data mining.

Illustrate strength and weakness of k-mean in comparison with k-medoid algorithm.

Explain the typical requirements of clustering in data mining.