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Unit-1
1a)Draw and explain the architecture of typical data mining systems
b)Differentiate OLTP and OLAP [2008]
2 A) Explain data mining as a step in the process of knowledge discovery
B) Differentiate operational database systems and datawarehousing [2008]
3 Explian various data reduction technique [2008]
4 a)Briefly discuss the various forms of presenting and visualizing the discovered
patterns [2008]
b)Discuss the objective measures of pattern interestingness [2008]
5)Explain the various data reductions techniques 2006]
6) a)What is query managements process ?explian briefy.
b)Explain how to directing queries?
c)Briefy discuss maximizing system resoures [2006]
d) Describe about query capture
unit-2
1
a)Briefy discuss the data smoothing techniques
b)Explain about concept hierarchy generation for categorical data [2008]
2)Discuss the issues regarding data ware house architecture [2008]
3)Briefly compare the following concepts with example
a)Snowflake schema , fact constellation, starnet query model
b)Data cleaning , data transformation, refresh
c)Discovery driven cube, multifeature cube, and virtual warehouse [2008]
4)What is partitioning data? Discuss with an example of a partitioned retial sales
fact table [2008]
5)Discuss about the summary information relating to the dataware housing [2006]
6)Explain the Significance of tuning the dataware housing [2006]
7)a)Discuss when a data mart is appropriate
b)Explain designing data marts
C)Discuss costs of data marting [2006]
8)a)Explain creation of star dimension
b)Explain structure of a starflake schema [2006]
9)Explain difference between designing a data warehouse and an OLTP
B)Explain fact table identification process [2006]
10)Explain designing dimension table
b)Explain designing starflake scheme [2006]
]
unit-3
1)List and describe any four primitives for specifying a data mining task [2008]
2)Describe why concept hierarchies are useful in data mining [2008]
3)Write the syntax of the following data mining primitives [2008]
a)The kind of knowledge to be mined
b)Measures of pattern interestingness
UNIT-4
1)How can we specify a data mining query for characterization with DMQL? [2008]
2)Describe the transformation of a data mining query to a relational query? [2008]
3)What are the differences between concept description in large data bases and
OLTP? [2008]
4)Explain about the graph deisplays of basics statistical class description [2008]
5)How can we perform attribute relevant analysis for concept description?explian
[2006]
6)Explain the measures of central tendency in detail [2006]
7)Describe the categorization of data access by role or job function? [2006]
\8)Describe the day to day operations of a dat a warhousing? [2006]
Unit-5
1)Which algorithm is a influential algorithm for mining frequent item sets for
Boolean association rules? Explain [2008]
2)What are additional rules constraints to guide mining ? explain [2008]
3)Discuss about association rule mining [2008]
4)What are the approaches for mining multilevel association rules?explian [2008]
5)Explain the apriori algorithm with example [2006]
6)How much CPU bandwidth is required and explain why? [2006]
7)What is a decision tree ?what are the advantages and disadvantages of decision
tree classifications? [2006]
8)Explain various query tuning methods in data warehouse. [2006]
Unit-6
1) Discuss about Backpropagation classification. [2008]
2) a) Explain decision tree induction classification.
b) Describe backpropagation classification. [2008]
3) a) Can any ideas from association rule mining be applied to classification?
Explain.
b) Explain training Bayesian belief networks.
c) How does tree pruning work? What are some enhancements to basic decision
tree induction? [2008]
4) What is splitting criteria? With an example explain about the [2006]
a) Class Histogram, and
b) Count Matrix
.
5) a) Explain about the Three basic levels of Testing.[2006]
b) Write in detail about the stages in Developing the Test Plan.
Unit-7
1) a) What major advantages does DENCLUE have in comparison with other
clustering algorithms?
b) What advantages does STING offer over other clustering methods?
c) Why wavelet transformation useful for clustering?
d) Explain about outlier analysis. [2008]
2) a) Define mean absolute deviation, z-score, city block distance, and minkowski
distance.
b) What are different types of hierarchical methods? Explain [2008].
3) a) Discuss about binary, nominal, ordinal, and ratio-scaled variables.
b) Explain about grid-based methods. [2008]
4) a) What are the categories of major clustering methods? Explain.
b) Explain about outlier analysis. [2008]
5) a) Which frequent itemset mining is suitable for text mining and why? Explain?
b) Discuss the relationship between text mining and information retrieval and
information extraction. [2006]
6) a) What is text clustering? Discuss the principles underlying text clustering.
b) Discuss the relationship between text mining and information retrieval and
information extraction. [2006]
7) a) Discuss the major algorithms of the sequence mining problem.
b) What is the event-prediction problem? Propose one algorithm to solve this
problem. [2006]
8) a) What is Page Rank? How a Page Rank is given for a page.
b) Discuss about Social Network.
c) How to define the similarity measure between pages.
[2006]
Unit-8
1) a) Explain spatial data cube construction and spatial OLAP.
b) Discuss about mining text databases. [2008]
2) a) Define spatial database, multimedia database, time-series database, sequence
database, and text database.
b) What is web usage mining? Explain with suitable example. [2008]
3) A heterogeneous database system consists of multiple database systems that are
defined independently, but that need to exchange transform information among
themselves and answer global queries. Discuss how to process a descriptive mining
query in such a system using a generalization-based approach. [2008]
4) a) How to mine Multimedia databases? Explain.
b) Define web mining. What are the observations made in mining the Web for
effective resource and knowledge discovery?
c) What is web usage mining? [2008]
5) a) Describe different similarity measures of time-series data.
b) Discuss the major features of the timeweaver algorithm. [2006]
6) a) What is Temporal DATA MINING? Explain about the types of Temporal
data.
b) Write in detail about the Temporal DATA MINING tasks. [2006]
7) a) How do you handle spatial and non-spatial data, while carrying out any mining
task?
b) Propose different neighborhood relationships that can be used for densitybased clustering of spatial data. [2006]
8) a) What is the underlying principles of “The Hidden Web”? How is text mining
related to web mining? What are the techniques of text mining?
b) Discuss about
i. Transverse & Intrinsic Links,
ii. Reference Nods & Index nodes. [2006]
9) a) What is Episode Discovery? In what way, it is similar to sequence mining.
b) Explain about the Episode Discovery process. [2006]