Page 48 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Data Warehousing and Data Mining

notes Fill in the blanks:
3. A ........................ is a collection of tables, each of which is assigned a unique name.

4. Data warehouses are constructed via a process of data cleaning, data integration, data
transformation, data loading and ........................
5. A ........................ is a set of records representing transactions, each with a time stamp, an
identifier and a set of items.
6. ................................... databases include video, images, audio and text media.
7. Time-series databases contain time related data such ........................
8. Data ........................ is a summarisation of general features of objects in a target class, and
produces what is called characteristic rules.
9. ........................ is based on the association rules.
10. Clustering is also called ........................ classification.
11. ........................ is a term used to describe the “process of discovering patterns and trends in
large data sets in order to find useful decision-making information.”

2.17 review Questions

1. What is data mining? How does data mining differ from traditional database access?
2. Discuss, in brief, the characterization of data mining algorithms.

3. Briefly explain the various tasks in data mining.
4. What is visualization? Discuss, in brief, the different visualization techniques.
5. Discuss the evolution of data mining as a confluence of disciplines.
6. What issues should be addressed by data mining algorithms and products? Why are they
relevant?
7. Discuss the need for metrics in data mining.
8. “Data mining can often have far reaching social implications.” Discuss this statement.
9. Discuss, in brief, important implementation issues in data mining.
10. Distinguish between the KDD process and data mining.

11. Discuss how database and OLTP systems are related to data mining.
12. Write a short note on the ER model. What advantage does it offer over a trivial DBMS?

answers: self assessment

1. (b) 2. (c)
3. relational database 4. periodic data refreshing

5. transaction database 6. Multimedia
7. stock market data 8. characterization
9. Association analysis 10. unsupervised
11. Data mining

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