Question 1: Define each of the given data mining functionalities: characterization, discrimination, association and correlation analysis, classification, prediction, clustering, and evolution analysis. Give illustrations of each data mining functionality, by using a real-life database with which you are familiar.
Question 2: In brief compare the given concepts. You might use an example to describe your point(s).
a) Snowflake schema, fact constellation, starnet query model.
b) Data cleaning, data transformation, refresh.
c) Enterprise warehouse, data mart, virtual warehouse.
Question 3: How is a data warehouse distinct from a database? How are they similar?
Question 4: What do you mean by Meta data? Describe with neat sketch.
Question 5: In real-world data, tuples with missing values for some attributes are a common occurrence. Explain different methods for handling this problem.
Question 6: Use a flowchart to summarize the given procedures for attribute subset selection:
a) Stepwise forward selection.
b) Stepwise backward elimination.
c) A combination of forward selection and backward elimination.
Question 7: What is the requirement of preprocessing data?
Question 8: Describe the term data integration.