Data Warehousing and Mining — Summer 2024

Explain various features of data warehouse.

Explain why data warehouses are needed for developing business solutions from today’s perspective.

Explain three-tier data warehouse architecture.

Give the comparisons of OLAP and OLTP system.

List challenges of data mining regarding data mining methodology and user interaction issues.

What is cuboid? Explain OLAP Operations with examples.

Explain star, snowflake, fact constellation schema for multidimensional database.

Explain various application area of data mining.

How to handle missing values in data set?

With the help of a suitable diagram explain the process of knowledge discovery from database.

Why preprocessing is required?

Explain min-max and Z-score normalization.

Explain Mean, Median, Mode, Variance and Standard Deviation with suitable database example.

Suppose a group of sales price records has been sorted as follow 6,9,12,13,15,25,50,70,72,92,204,232 partition them into three bins by equal frequency. Perform data smoothing by bin mean.

Explain association rules with confidence and support.1

State the Apriori Property. Find frequent item-sets and association rules using Apriori algorithm on the following data set with minimum support count is 2 and minimum confidence=75%. Sr.No TID List of items_IDs 1 T100 I1,I2,I5 2 T200 I2,I4 3 T300 I2,I3 4 T400 I1,I2,I4 5 T500 I1,I3 6 T600 I2,I3 7 T700 I1,I3 8 T800 I1,I2,I3,I5 9 T900 I1,I2,I3

Write the difference between classification and predication.

Explain rule based classification.

Explain Naïve Bayesian classifier with example. Why it is called Naïve?

List strength and weakness of neural network as classifier.

Define True Positive, True Negative, False Positive and False Negative.

Explain decision tree algorithm with suitable example.

Write short note on web mining.

Explain text mining.

Explain k-means clustering algorithm. .2