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QUESTION BANK
UNIT-1
Short Answer Questions
QUESTIONS Blooms taxonomy
level
Course
Outcome
1.Define data mining? Understand CO1
2.Explain the functionalities of data mining? Understand CO1
3.Interpret the major issues in data mining? Knowledge CO1
4.Name the steps in knowledge discovery? Knowledge CO1
5.Distinguish between data ware house and data mining? Analyze CO1
Long Answer Questions

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1.Describe Data Mining? In your answer explain the Understanding CO1

following:
a. Is it another hype?
b. Is it simple transformation of technology developed from
databases, statistics and machine learning?
c. Explain how the evolutions of database technology lead to
data mining?
d. Describe the steps involved in data mining when viewed as
knowledge discovery process?
2.Discuss briefly about data smoothing techniques? Creating CO1
3.List and describe the five primitives for specifying the data Analyzing CO1
mining tasks?
4.Define data cleaning? Express the different techniques in
handling the missing values?
Understanding

te s CO1

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5.Explain mining of huge amount of data (eg: billions of Analyzing CO1
tuples) in comparison with mining a small amount of data
(Eg: data set of few hundred of tuples).

UNIT-2
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Short Answer Questions
QUESTIONS Blooms taxonomy Course

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level Outcomes
1.Explain the frequent item set?

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2. Explain about maximal frequent items set and closed item
set?

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Understanding
Knowledge
CO2
CO2

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3.Name the steps in association rule mining? Understand CO2

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4.Explain the efficiency of APRIORI algorithm Analyze CO2
5.Define item set? Interpret the support and confidence rules Understand CO2

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for item set A and item set B?
Long Answer Questions
1.Discuss which algorithm is an influential algorithm for
mining frequent item sets for Boolean association rules?
Analysis CO2

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Explain with an example?
2.Describe the FP-growth algorithm with an example?
3.Explain how to mine frequent item sets using vertical data
format?
4.Explain how to mine the multi dimensional association
rules from relational data bases and data ware houses?
Analysis
Understand

Understand
CO2
CO2

CO2

5.Explain the APRIORI algorithm with an example? Analysis CO2

UNIT-3
Short Answer Questions
QUESTIONS Blooms taxonomy Course
level Outcomes
1.State classification and define regression analysis? Understand CO2

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2.Name the steps in data classification and define training Knowledge CO2
tuple?
3.Explain the IF-THEN rule in classification? Analysis CO3
4.What is tree pruning and define the Naïve Bayes Knowledge CO3
classification?
5.Explain the decision tree? Understand CO3
Long Answer Questions
1.Explain about the classification and discuss with an Analysis CO2
example?
2.Summarize how does tree pruning work? What are some Understanding CO2
enhancements to basic decision tree induction?
3.Describe the working procedures of simple Bayesian
classifier?
4.Discuss about Decision tree induction algorithm?
Analysis

Evaluate

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CO3

5.Explain about IF-THEN rules used for classification with an

example and also discuss about sequential covering

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Knowledge CO3

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algorithm?
UNIT-4

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Short Answer Questions
QUESTIONS Blooms Course

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taxonomy level Outcomes

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1.Define clustering? Knowledge CO3

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2.llustrate the meaning of cluster analysis?
3.Explain the different types of data used in clustering?
4.Explain the fields in which clustering techniques are used?
5.State the hierarchical methods?
Knowledge
Knowledge
Understand
Understand
CO3
CO4
CO4
CO4
Long Answer Questions

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1.Discuss various types of data in cluster analysis?
2.Explain the categories of major clustering methods?
Analysis
Understand
CO3
CO3

k-means?

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3.Explain in brief about k-means algorithm and portioning in

4.Describe the different types of hierarchical methods?

5.Discuss about the outliers? Explain the weakness and
strengths in hierarchical clustering methods?
Analysis

Knowledge
Knowledge
CO4

CO4
CO4

UNIT-5
Short Answer Questions
QUESTIONS Blooms Course
taxonomy level Outcomes
1.Define Web mining and text mining? Knowledge CO4
2.Write a short note on web content mining. Understand CO4
3.What are the features of Unstructured text mining. Knowledg CO4
4. Write a short note on web structure mining. Understand CO4
5.Write a short note on web usage mining. Understand CO4

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Long Answer Questions

1.Explain about authoritative and Hub pages? Knowledge CO4
2.Give taxonomy of web mining activities.For what purpose Understand CO4
web usage mining is used?
3. what activities are involved in web usage mining? Knowledge CO4
4.Explain Episode rule discovery for texts. Knowledge CO4
5.Write a short note on Text clustering. Understand CO4
Objective Questions:
UNIT-1
1. The Synonym for data mining is
(a)Data warehouse (b)Knowledge discovery in database (c)ETL (d)Business intelligence
2. Data transformation includes which of the following?
a) A process to change data from a detailed level to a summary level
b). A process to change data from a summary level to a detailed level
c) Joining data from one source into various sources of data
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d). Separating data from one source into various sources of data

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3. Which of the following process includes data cleaning, data integration, data transformation, data
selection, data mining, pattern evaluation and knowledge presentation?
A. KDD process

(a)Business requirements level

(c) Detailed models level
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B. ETL process C. KTL process D. None of the above
4. At which level we can create dimensional models?
(b) Architecture models level
(d)Implementation level (e)Testing level.

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5. What are the specific application oriented databases?

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A. Spatial databases, B. Time-series databases, C. Both a & b D. None of these
UNIT-2

A. Binary attribute.
attribute.
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1. Association rules are always defined on________.
B. Single attribute.

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C. Relational database. D. Multidimensional

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2. __________ is data about data.

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A. Metadata. B. Microdata. C. Minidata D. Multidata.
3. Which of the following is the data mining tool?

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A. C. B. Weka. C. C++. D. VB.
4. Capability of data mining is to build __________ models.
A. Retrospective. B. Interrogative. C. Predictive. D. Imperative.
5. The _________is a process of determining the preference of customer’s majority.
A. Association. B. Preferencing. C. segmentation. D. classification.
UNIT-3
1. Another name for an output attribute.
a. predictive variable
b. independent variable
c. estimated variable
d. dependent variable
2. Classification problems are distinguished from estimation problems in that
a. classification problems require the output attribute to be numeric.
b. classification problems require the output attribute to be categorical.
c. classification problems do not allow an output attribute.

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d. classification problems are designed to predict future outcome.

3. Which statement is true about prediction problems?
a. The output attribute must be categorical.
b. The output attribute must be numeric.
c. The resultant model is designed to determine future outcomes.
d. The resultant model is designed to classify current behavior.
4. Which statement about outliers is true?
a. Outliers should be identified and removed from a dataset.
b. Outliers should be part of the training dataset but should not be present in the test
data.
c. Outliers should be part of the test dataset but should not be present in the training

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data.

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d. The nature of the problem determines how outliers are used.
e. More than one of a,b,c or d is true.
5. Which statement is true about neural network and linear regression models?
a. Both models require input attributes to be numeric.
b. Both models require numeric attributes to range between 0 and 1.
c. The output of both models is a categorical attribute value.

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e. More than one of a,b,c or d is true.
Unit IV
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d. Both techniques build models whose output is determined by a linear sum of
weighted input attribute values.

Multiple Choice Questions

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1. A trivial result that is obtained by an extremely simple method is called _______.

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A. naive prediction. B. accurate prediction. C. correct prediction. D. wrong prediction.

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2. K-nearest neighbor is one of the _______.

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A. learning technique. B. OLAP tool. C. purest search technique. D. data warehousing tool.
3. Enrichment means ____.

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A. adding external data. B. deleting data. C. cleaning data. D. selecting the data.

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4. Clustering methods are______.

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A. Hierarchical. B. Agglomarative. C. PAM algorithm. D. K-nearest neighbor. E. All the
above
UNIT-V
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1. HITS abbreviation in Web Structure?
a. Hyperlink-Index Topic Search b. Hyperlink-Induces Topic Search
c. Hyperlink-Identification Text Search d. Hyperlink-Index Text Search
2. Preprocessing Web log activity is?
a. Count patterns that occur in sessions b. Remove extraneous Information
c. Count Page references d. Pattern Setting
3. Periodic Crawler defines?
a. Visits Portions of the Web b. Selectively searches the Web
c. Visits pages related to a particular subject d. Collect Information from visited pages
4. Which is assigns relevance score to each page based on crawl topic?
a. Distiller b. Hub pages
c. Hypertext Classifier d. scores

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5. What is main Objective of web mining?

a. Web Component, Score and Usage Mining b. Web Control, Text and Utility Mining
c. Web Content, Score and Utility Mining d. Web Content, Structure and Usage

Fill in the blanks:

Unit 1

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1. Data Mining_________predicts future trends & behaviors, allowing business managers to
make proactive, knowledge-driven decisions

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2. Data Cleaning is a process that removes …outliers………………..
3. The output of KDD is useful information

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4. Data Discrimination is a comparison of the general features of the target class data objects
against the general features of objects from one or multiple contrasting classes

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5. Strategic value of data mining is time-sensitive

Unit 2

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1. ____Referencing_________ is a process of determining the preference of customer's
majority.

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2. __Data Mart__________ is a metadata repository

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3. The two steps in Apriori includes …join…………. and ……prune……..
4. FP Growth stands for ……Frequent pattern growth………………..

Unit 3

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5. Use normalization by decimal scaling to transform the value 35 for age……0.35………..

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1. Classification is the process of finding a model (or function) that describes and
distinguishes data classes or concepts.
2. Data mining methods discard outliers as noise or exceptions.
3. Prediction also used for to know the unknown or missing values.
4. In a decision tree, leaf nodes represent class labels or class distribution.
5. Decision Tree is constructed in a top-down recursive divide-and-conquer manner.
Unit 4:

1. A cluster analysis is the process of analysing the various clusters to organize the different
objects into meaningful and descriptive object.
2. …Agglomerative…………… clustering follows bottom up strategy
3. PAM means… “partition around medoids”……. …………………..
4. Bayesian classifiers exhibited high accuracy and speed when applied to large databases.

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5. Most data mining methods discard outliers as noise or exceptions.

Unit 5:

1. Hub Pages Contain links to many relevant pages

2. PageRank, CLEVER Techniques used in Web Structure Mining
3. Weighting is used to provide more importance to backlinks coming form important pages
4.PageRank equation PR(p)=c(PR(1)/N1 +...+PR(n)/Nn)
5.What is the use of CLEVER? Finding both Authoritative and Hub pages.
XI.WEBSITES:
1. www.autonlab.org/tutorials : Statistical Data mining Tutorials
2. www- db.standford.edu /`ullman/mining/mining.html : Data mining lecture notes

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3.ocw.mit.edu/ocwweb/slon-School-of-management/15-062Data- MiningSpring2003/course

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home/index.htm: MIT Data mining open courseware
XII.EXPERT DETAILS:
1. Jiaweihan, Abel Bliss Professor, Department of Computer Science, Univ. of Illinois at Urbana-
Champaign Rm 2132, Siebel Center for Computer Science

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2. Michelinekamber, Researcher,Master's degree in computer science (specializing in artificial
intelligence) from Concordia University, Canada

XIII.JOURNALS:

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3. Arun k pujari, Vice Chancellor, Central University Of Rajasthan - Central University Of
Rajasthan

1. Data warehousing, data mining, OLAP and OLTP technologies are essential elements to support
decision-making process in Industries

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2. Effective navigation of query results based on concept hierarchy

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3. Advanced clustering data mining text algorithm

1. Fundamentals of Data Mining

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2. Data Mining functionalities s
XIV.LIST OF TOPICS FOR STUDENT SEMINARS:

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3. Classification of data mining system

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4. Pre-processing Techniques

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5. APRIORI Algorithm

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6. FP-Growth Algorithm
7. Spatial data mining

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8. Web mining
9. Trends and applications of data mining
10. Text mining

XV.CASE STUDIES / SMALL PROJECTS:

Case study-1:
Search queries on biomedical databases, such as PubMed, often return a large number of results,
only a small subset of which is relevant to the user. Ranking and categorization, which can also be
combined, have been proposed to alleviate this information overload problem. Results
categorization for biomedical databases is the focus of this work. A natural way to organize
biomedical citations is according to their MeSH annotations. First, the query results are organized
into a navigation tree

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