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PART – A
Q.No.
Question
1
Define clustering
How can the initial number of clusters for k-means algorithm be
2
estimated
3
Can you Pick K in a K-Means Algorithm?
4
What are the problems faced if clustering exists in non-Euclidean
Point out the conclusions drawn from choosing clustroid?
5
8
Define Bayes Theorem
9
Discuss the number of clusters?
10
What is diagnotics?
Examine the use of object Attributes?
13
14
15
16
17
18
BTL5
BTL2
BTL4
Evaluating
Understanding
Analyzing
BTL4
Analyzing
BTL6 Creating
BTL 1 Remembering
What is unit of measure?
BTL3 Applying
BTL 1 Remembering
Summarize about Rescaling?
BTL2 Understanding
What is metoids?
What is Customer segmentation
Describe the prediction trees
Analyze on internal nodes and leaf nodes
BTL2 Understanding
BTL 1 Remembering
CO
R
12
Applying
BTL2 Understanding
BTL 1 Remembering
U
11
BTL3
AP
P
7
Compare and contrast the relationship between centroids and
clustering
Generalize the initialization of K-Means algorithm?
6
BT Level Competence
BTL 1 Remembering
What is purity of node
Point out the CART
BTL2
Understanding
BTL4 Analyzing
BTL 1 Remembering
BTL4
Analyzing
BTL3
Applying
Explain the K-means clustering algorithm with an example.(13)
BTL2
Understanding
2
i.
ii.
Define K-Means algorithm(6)
Examine how the data is processed in BFR Algorithm(7)
BTL1
Remembering
3
What are the main features of GRGPF Algorithm and explain it?(13)
BTL1
Remembering
4
Summarize the hierarchical clustering in Euclidean and non-Euclidean
Spaces with its efficiency?(13)
BTL2
Understanding
5
Describe the various hierarchical methods of cluster analysis. (13)
BTL2
Understanding
ST
19
20
1
6
7
Illustrate Naïve Bayes
PART – B
Explain and list the different hierarchical clustering techniques and
BTL4
explain any one. (13)
i.
Given a one dimensional dataset {1, 5, 8, 10, 2} use the
BTL6
agglomerative clustering algorithms with the complete link with
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Analyzing
Creating
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relationship. By using the maximal lifetime as the cutting
threshold, how many clusters are there? What is their
membership in each cluster? (6)
ii.
State and explain the clustering in non-Euclidean space with
example. (7)
Describe about Market-Basket model.(13)
9
10
11
12
13
3
4
Illustrate in detail about Decision tree in R.(13)
Explain in detail about Naïve Bayes Theorem, Classifier, Smoothing
and Diagnostics. (13)
PART – C
Use the k-means algorithm and Euclidean distance to cluster the
following 8 examples into 3
clusters:A1=(2,10),A2=(2,5),A3=(8,4),A4=(5,8),A5=(7,5),A6=(6,4),A
7=(1,2),A8=(4,9).Suppose that the initial seeds(centers of each cluster)
are A1,A4 and A7.Run the k-means algorithm for 1 epoch only. At the
end of this epoch show
(i)
The new clusters (5)
(ii)
The centers of the new clusters (6)
(iii) How many more iterations are needed to coverage? Draw the
result for each epoch. (4)
Develop decision tree with an example to predict whether customers
will buy a product(15)
Explain in detail about evaluate the decision tree algorithm(15)
U
2
Generalize about general algorithm and decision tree algorithm. (13)
ST
1
Explain in detail about the applications of clustering. (13)
CO
R
14
Demonstrate about the two clustering techniques with suitable
example.(13)
Illustrate about the clustering? Explain it with proper example.(13)
Understanding
BTL1
Remembering
BTL3
Applying
BTL1
Remembering
BTL4
Analyzing
BTL 6
Creating
AP
P
8
BTL2
Explain in detail about two methods of using the naïve Bayes classifier
in R with examples.(15)
UNIT III
BTL3
Applying
BTL4
Analyzing
BTL5
Evaluating
BTL 6 Creating
BTL5
Evaluating
BTL 6 Creating
ASSOCIATION AND RECOMMENDATION SYSTEM
Advanced Analytical Theory and Methods: Association Rules - Overview - Apriori Algorithm - Evaluation
of Candidate Rules - Applications of Association Rules - Finding Association& finding similarity Recommendation System: Collaborative Recommendation- Content Based Recommendation - Knowledge
Based Recommendation- Hybrid Recommendation Approaches.
PART – A
BT
Q.No.
Question
Competence
Level
Define apriori algorithm
1
BTL1 Remembering
State the use of Apriori algorithm in data mining
2
BTL1 Remembering
3
State market basket analysis
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BTL1
Remembering
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What is the logic behind association rule?
BTL2
Understanding
5
What is Prune
BTL2
Understanding
6
Define Confidence
BTL1
Remembering
7
What do you mean by lift
BTL2
Understanding
8
Show the advantage of leverage
9
10
What is frequent itemset generation
Analyze the Validation and testing
BTL3
BTL1
Applying
Remembering
BTL4
Analyzing
11
Demonstrate the approaches to improve Apriori efficiency
BTL3
Applying
12
How are interesting rules identified?
BTL4
Analyzing
13
Examine the broad classification of Recommendation systems?
14
Summarize the interesting rules distinguished from coincidental rules?
What is utility matrix
15
AP
P
4
BTL3
BTL 5
Applying
Evaluating
BTL4
Analyzing
Examine about item profile
Give the Content Based recommendation system
BTL1
BTL 5
Remembering
Evaluating
18
Explain collaborative filtering system
Define knowledge based Recommendation
BTL4
BTL2
Analyzing
Understanding
Give the definition Hybrid recommendation
BTL2
Understanding
19
20
CO
R
16
17
PART – B
Explain the apriori algorithm for mining frequent item sets with an
example. (13)
2
Illustrate how will you find Association Rules with High confidence(13)
Evaluate Apriori Algorithm in detail for the following super market
Scenarios(13)
4
5
6
abora
ST
3
U
1
Transacti Onion
Potato
Burger
Milk
Gee
on ID
T1
1
1
1
0
0
T2
0
1
1
1
0
T3
0
0
0
1
1
T4
1
1
0
1
0
T5
1
1
1
0
1
T6
1
1
1
1
1
Describe the Recommendation systems? Clearly explain the two
applications for Reccomdation systems.(13)
Discuss in detail about any one Ranking algorithm used by Search
Engines?(13)
Explain Recommendation based on User Ratings using appropriate
example.(13)
tive filtering and content based systems. (13)
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BTL1
Remembering
BTL3
Applying
BTL5
Evaluating
BTL1
Remembering
BTL2
Understanding
BTL1
Remembering
BTL2
Understanding
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8
9
10
11
12
13
14
Explain collaborative filtering based recommendation system. (13)
Differentiate between lexical similarity and semantic similarity of
documents.(13)
Explain in detail about Frequent item set generation and rule generation.
(13)
Explain in detail about evaluation of candidate rule. (13)
BTL2
Understanding
BTL1
Remembering
BTL1
Remembering
Outline in detail about the application of association rule. (13)
Generalize in detail about utility matrix and long tail. (13)
BTL3
Applying
BTL 6
Creating
Explain in detail about discovering features of documents. (13)
BTL3
Applying
PART – C
2
3
4
AP
P
A database has five transactions. Let min sup = 60% and min conf=80%
TID
ITEMS
T100 Milk, Onion, Nuts, Kiwi, Egg, Yoghurt
T200 Dhal, Onion, Nuts, Kiwi, Egg, Yoghurt
T300 Milk, Apple, Kiwi, Egg
T400 Milk, Curd, Kiwi,Yoghurt
T500 Curd, Onion, Kiwi, Ice cream,Egg
Find all frequent item sets using Apriori method(15)
Narrate in detail about a model for Recommendation system.(15)
CO
R
1
BTL6
Creating
BTL 5
Evaluating
Illustrates with an example the application of the Apriori algorithm to a
relatively simple case that generalizes to those used in practice. Show how to
use the Apriori algorithm to generate frequent item sets and rules and to BTL6
evaluate and visualize the rules.(15)
Explain in detail about Hybrid and Knowledge based
recommendation.(15)
Analyzing
STREAM MEMORY
U
UNIT IV
BTL 4
Creating
ST
Introduction to Streams Concepts – Stream Data Model and Architecture - Stream Computing, Sampling Data
in a Stream – Filtering Streams – Counting Distinct Elements in a Stream – Estimating moments – Counting
oneness in a Window – Decaying Window – Real time Analytics Platform(RTAP) applications - Case Studies Real Time Sentiment Analysis, Stock Market Predictions. Using Graph Analytics for Big Data: Graph
Analytics
PART – A
Q.No.
Question
Differentiate between data stream mining and traditional data mining
1
BT
BTL4
Competence
Analyzing
BTL3
Applying
3
Illustrate examples can you find for stream sources?
How are moments estimated?
BTL2
Understanding
4
List out the applications of data stream.
BTL4
Analyzing
5
Compute the surprise number (second moment) for the stream 3, 1, 4, 1, BTL1
3, 4, 2, 1, 2. What is the third moment of this stream?
Define decaying window
BTL1
Remembering
Outline the need for sampling data in a stream
Analyze the term filtering a data stream.
BTL5
Evaluating
BTL3
Applying
2
6
7
8
9
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FROM STUCOR APP
Remembering
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10
Give the advantages of the algorithm used in estimating moments
BTL1
Remembering
11
Why stream data systems?
Why do you think data stream management is relevant in data mining?
BTL2
BTL2
Understanding
Understanding
How oneness is counted in window
BTL5
Evaluating
13
14
15
16
17
18
19
20
What would result if the cost of exact counts doesn‟t match?
BTL3
Illustrate how would you show your understanding of Market-Basket
BTL3
Data?
What is sentiment analysis?
BTL1
Compare and contrast RTAP (Real Time Analytics Platform) and RTSA BTL5
(Real Time Sentiment Analysis)?
Prove by induction on m that 1+3+5+· · · +(2m−1) = m2
BTL4
List any 4 online tool to perform sentiment analysis
BTL1
AP
P
12
Generalize information would you use to substitute the view of streams
over databases?
Applying
Applying
Remembering
Evaluating
Analyzing
Remembering
BTL6
Creating
What are streams? Explain stream data model with its architecture.(13)
BTL1
Remembering
What is decaying window? briefly explain it with an example (13)
BTL1
Remembering
i. Write a short note on sampling in Data Streams.(7)
ii. What are the applications of data stream.(6)
i. List some common online tools used to perform sentiment
analysis.(6)
ii. What do you understand by sentiment analysis?(7)
BTL2
Understanding
BTL1
Remembering
Explain any one algorithm to count number of distinct elements in a
data stream. (13)
Describe about Stream clustering and parallel clustering. (13)
BTL2
Understanding
BTL1
Remembering
Discuss in detail about characteristics of a social network as a graph.
(13)
BTL3
Applying
BTL6
Creating
Discuss the concept of decaying window in detail.(13)
BTL3
Applying
i.
BTL2
Understanding
BTL4
Analyzing
Show how the mining concept used in real time sentiment analysis? (13) BTL3
Applying
How is sentiment analysis playing a major role in data mining? (13)
Analyzing
PART – B
CO
R
1
2
3
4
U
5
6
8
ST
7
i. With a neat sketch, explain the architecture of data-stream
management system.(6)
ii. Outline the algorithm used for counting distinct elements in a data
stream.(7)
9
10
11
12
13
Explain in detail about how data analysis used in Stock Market
Predictions(7)
ii.
Describe in detail about the usage of data analysis in Weather
forecasting predictions. (6)
Explain the concept of Bloom Filter with an example.(13)
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BTL4
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14
1
2
Explain in detail about Alon-Matias-Szegedy algorithm for second
moments. (13)
PART – C
How does the Big Data Stream Analytics Framework (BDSAF)works
and explain with a neat architecture diagram (15)
Taking stock market preconditions as a case study, elaborate on the
Real-time Analytics Platform (RTAP). Present the assumptions mode.
(15)
4
UNIT V
Applying
BTL6
Creating
BTL6
Creating
BTL5
Evaluating
BTL5
Evaluating
AP
P
i. Classify approaches would you use to estimate the moments?(7)
ii. Examine is the function cost of exact counts?(8)
Can you identify the phases involved in real time data analyticsdeployment to production? Analyze. (15)
3
BTL3
NOSQL DATA MANAGEMENT FOR BIG DATA AND
VISUALIZATION
ST
U
CO
R
NoSQL Databases : Schema-less Models‖: Increasing Flexibility for Data Manipulation-Key Value StoresDocument Stores - Tabular Stores - Object Data Stores - Graph Databases Hive - Sharding –- Hbase –
Analysing big data with twitter - Big data for E-Commerce Big data for blogs - Review of Basic Data
Analytic Methods using R.
PART – A
Q.No.
Question
BT
Competence
What
is
NoSQL
database
1
BTL1 Remembering
What is Key Value data store?
2
BTL1 Remembering
3
Compare document store vs Key value store
BTL1 Remembering
4
BTL1 Remembering
Provide your own definition of what big data means to your
organization?
Outline the sharding?
5
BTL 5 Creating
6
Identify three “big data” sources either within or external to your
BTL2 Understanding
organization that would be relevant to your business
Define Tabular store
7
BTL2 Understanding
Summarize the features of Hive.
8
BTL4 Analyzing
9
10
What is Hive in Big data?
List out any three business challenges in an organization
11
Point out the aspects of adopting big data techniques
12
Figure out the process of validating big data
13
14
Name the dimensions for measuring the quality of information used for
big data analytics
Define object data stores.
15
16
Justify how twitter data is useful for analyzing big data.
Point out top data analytic tools.
17
18
Define R.
What is a Graph database?
19
Enumerate the term Graph Analytics.
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BTL4
Analyzing
BTL3
Applying
BTL3
Applying
BTL3
BTL4
Applying
Analyzing
BTL1
BTL2
Remembering
Understanding
BTL5
BTL1
Evaluating
Remembering
BTL1
Remembering
BTL5
Evaluating
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20
Describe a pilot application for graph analytics
BTL4
Analyzing
BTL3
Applying
PART – B
List the classification of NoSQL Databases and explain about Key
Value Stores. (13)
1
2
3
8
9
10
12
13
4
Remembering
Applying
With suitable examples differentiate the applications, structure,
working and usage of different NoSQL databases (13)
BTL2
Understanding
Differences between SQL Vs NoSQL explain it with suitable example.
(13)
i. Explain Hive Architecture(6)
ii. Write down the features of Hive(7)
Explain the types of NoSQL data stores in detail. (13)
Discuss in detail about the characteristics of NoSQL databases(13)
BTL2
Understanding
BTL2
Understanding
BTL3
Applying
BTL4
BTL1
Analyzing
Remembering
BTL4
Analyzing
BTL6
Creating
BTL4
Analyzing
BTL5
Evaluating
BTL5
Evaluating
BTL 6
Creating
Explain in detail about Market and Business drives for Big data
Analytics. (13)
What is HBase? Give detailed note on features of HBASE(13)
i. What is the purpose of sharding? (6)
ii. Explain the process of sharding in MongoDB. (7)
PART – C
Analyze the use of Hive. How does Hive interact with Hadoop explain
in detail.(15)
Formulate how big data analytics helps business people to increase
their revenue. Discuss with any one real time application.(15)
Draw insights out of any one visualization tool.(15)
ST
14
3
BTL1
BTL3
CO
R
7
2
Explain about Graph databases and descriptive Statistics(13)
Write short notes on
i.
NoSQL Databases and its types(7)
ii. Illustrate in detail about Hive data manipulation, queries, data
definition and data types(6)
U
6
1
Remembering
AP
P
4
5
BTL1
Describe the system architecture and components of Hive and
Hadoop(13)
What is NoSQL? What are the advantages of NoSQL? Explain the types
BTL1
of NoSQL databases. (13)
Explain in detail about brief history of NoSQL. Explain in detail about
ACID vs. BASE.(15)
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Remembering
ST
U
CO
R
AP
P
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