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UNIT WISE IMPORTANT QUESTIONS
UNIT I IMPORTANT SUBJECTIVE QUESTIONS
1.Explain data mining as a step process of knowledge discovery. Mention the Functionalities
of Data mining.
2.Explain the various Data pre-processing techniques. How data reduction helps in data pre-
processing.
3. What is the need of dimensionality reduction? Explain any two techniques for
dimensionality reduction.
4.Explain Feature subset selection architecture and methods.
b)Give Similarity and dissimilarity Measures ?
5.What is DataMining?List and describe the motivating challenges of Datamining
6.Write short notes on i) jacquard coefficient ii)correlation iii) similarity measures for binary
data
7.what is cleaning ?what are different techniques for handling missing values?
8.write short notes on data descretization?
UNIT II IMPORTANT SUBJECTIVE QUESTIONS
1. Make a comparison of Apriori and ECLAT algorithms for frequent item set mining in
transactional databases. Apply these algorithms to the following data:
TID LIST OF ITEMS
1 Bread, Milk, Sugar, TeaPowder, Cheese, Tomato
2 Onion, Tomato, Chillies, Sugar, Milk
3 Milk, Cake, Biscuits, Cheese, Onion
4 Chillies, Potato, Milk, Cake, Sugar, Bread
5 Bread, Jam, Mik, Butter, Chilles
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6 Butter, Cheese, Paneer, Curd, Milk, Biscuits
7 Onion, Paneer, Chilies, Garlic, Milk
8 Bread, Jam, Cake, Biscuits, Tomato
2. a) Explain the procedure to mining closed frequent data item sets.
b) Explain, how can you improve the performance of Apriori algorithm.
3.Can we design a method that mines the complete set of frequent item sets without candidate
generation? If yes, explain with example table mentioned below.
List of items
TID
001 Milk, dal, sugar,
bread
002 Dal, sugar,
wheat,jam
003 Milk, bread,
curd, paneer
004 Wheat, paneer,
dal, sugar
005 Milk, paneer,
bread
006 Wheat ,dal,pane
er,bread
4. Illustrate with an A-priori algorithm for the given dataset above.
5.Discuss in brief about i)maximal frequent itemset ii)closed frequent itemset
6.Discuss in brief about following
i) Apriori principle ii)support and confidence iii)Monotonicity property
UNIT III IMPORTANT SUBJECTIVE QUESTIONS
1.a)Describe the data classification process with a neat diagram. How does the Naive
Bayesian classification works? Explain.
2.Describe Bayesian Belief Network algorithm how it works?explain
3.a)What is prediction? Explain the various prediction techniques.
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b)Explain about Decision tree Induction classification technique.
4.Explain how Rule Based method is used for classification in data mining process.
5.Explain in detail hold out method for evaluating classifier.
6.Explain about evaluating the performance of classifier.
7 (a) Explain Rule based classifiers. (b) Explain the Nearest neighbor classification.
8.What are the Attribute selection procedures to build decision tree explain them briefly
9.Explain ID3 Algorithm b) what Tree pruning how many type techniques explain ?
10.Explain C4.5 and CART Algorithms.
UNIT IV IMPORTANT SUBJECTIVE QUESTIONS
1. Define clustering and describe the categorization of major clustering methods
2 Differentiate between agglomerative and divisive hierarchical clustering
3 Explain different types of data types used in cluster analysis
4 Discuss k-means algorithm
5 Discuss about density based methods
6 What is meant by Outlier analysis. Discuss about any one outlier detection
7 Explain about statistical based outlier detection and deviation based outlier detection
8. Explain hierarchical methods of clustering.
9. Discuss different types of clustering methods
10. Explain in detail DBSCAN clustering algorithm.
11 Define cluster analysis. List and explain applications of cluster analysis.
UNIT V IMPORTANT SUBJECTIVE QUESTIONS
1.a)Discuss About Web Mining ?b).Discuss Web Content Mining?
2.a)Discuss Web Structure mining?b).Discuss Web Usage mining?
3.Discuss in detail about episode rule discovery for texts?
4.Discuss about following techniques for modelling web topology
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i) Page Rank ii)Social Network
5.What are text clustering methods?explain in detail
6.What are the applications of Web and Text Mining?Explain in detail about text mining
7.Discuss in detail about hierarchy of categories
8.Discuss about Information Retrieval and information extraction
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