Data warehousing & Data mining

K. Thammi Reddy

Associate Professor


Chapter Wise Assignment


1.                  Introduction


a.       How is a Data Warehousing from a database? How are they similar?

b.      Define Data Mining? Specify some synonym for Data Mining.

c.       Are all of the patterns interesting?

d.      Define each of the following Data Mining functionalities.

i.                     Characterization

ii.                   Discrimination

iii.                  Association

iv.                 Classification and Prediction

v.                   Clustery

vi.                 Evolution Analysis

e.       How do you classify the Data Mining System?



2.                  Data Warehousing and OLAP Technology for Data Mining


a.       Differentiate between OLTP and OLAP?

b.      Define a Data warehousing? How are organizations using the information form Data Warehousing?

c.       Explain about a Multidimensional Data Model? What are the different schemes that are used to represent multi dimensional model?

d.      What is a concept hierarchy? Explain different OLAP Operation in the Multidimensions Data Model?

e.       Explain with neat sketch a three tier Data Warehousing Architecture?

f.        What are the different types of OLAP servers? Explain about Meta Data repository?


3.                  Data Pre Processing.


a.       What is the need of Data Pre Processing?

b.      Explain about Data Cleaning?

c.       Explain about Data Integrity and Transformation?

d.      Explain about Data Reduction?

e.       Explain about Discretization and Concept hierarchy generation.


4.                  Data Mining primitive, languages and system Architectures.


a.       What are the different Data Mining primitives? Explain.

b.      Explain the Syntax for the Data mining primitives.

c.       Explain the Architecture of Data Mining System.

5.                  Concept Description Characterization and Comparison.


a.       Define concept Description.

b.      Explain about Data Generalization and Summarization based characterization (AOI).

c.       What is the need of Performs Attribute Relevance Analysis?

d.      How do you measure central tendency Dispersion of Data?


6.                  Mining Association Rules in Large Databases.

a.       How do you classify Association Rules?

b.      Explain about Apriori Algorthithm for finding frequent item sets.

c.       What is an Iceberg Query?

d.      Explain about Multilevel Association Rules?

e.       Explain about Association Mining to Correlation Analysis.


7.                  Classification and Prediction.


a.       Define classification and Prediction?

b.      Explain about different classification methods?

c.       Explain about Bayesian Classification

d.      What is Bach Propagation? Explain about Back Propagation?

e.       Explain about K Nearest Neighbor classifies and other methods.

f.        Explain about prediction?


8.                  Cluster Analysis.


a.       Define cluster Analysis?

b.      Explain different types of data used in clusters analysis?

c.       Explain about different methods used in Cluster?

d.      Explain in detail about density Based Method?

e.       Explain in detail about Grid based Methods?

f.        Explain in detail about outlier analysis.