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Data Mining Question Bank: Graph Theory (DIT-S508) - Unit 1, Lecture notes of Data Mining

Kanpur UniversityData Mining

This pdf contains notes of a subject of Information Technology department of UIET, Kanpur.

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Department of Information Technology
UIET, CSJM University, Kanpur
Graph Theory (DIT-S508)
Question Bank
Unit-1: Data Mining and Data Preprocessing
1. Give two reasons for the dirty data being extracted from source systems?
2. What is data mining?
3. Mention Data mining functionality, classification, prediction, clustering and evolution
analysis?
4. What are the challenges in methodology of Data Mining technology?
5. What defines a Data Mining Task Explain at least 5 primitives?
6. What is knowledge discovery?
7. What is a data? What do you mean by quality of data?
8. What is data preprocessing?
9. Explain the following
1. Sampling
2. Dimensionality reduction
3. Discretization and binarization
10. Explain the similarity and dissimilarity between 2 objects.
11. What is Ecludian distance? Write the generalized Minkowski distance metric for various
values r.
12. What is simple matching coefficients and Jaccard coefficient? Explain with examples.
13. What is meant by cousine similarity? Explain with example.
14. What are the issues related to proximity measures?
15. Discuss on selection on right proximity measures.
16. What is KDD? Explain the different stages of KDD process.
17. Explain the factors used for measuring the central tendency.
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Department of Information Technology

UIET, CSJM University, Kanpur

Graph Theory (DIT-S508)

Question Bank

Unit-1: Data Mining and Data Preprocessing

  1. Give two reasons for the dirty data being extracted from source systems?
  2. What is data mining?
  3. Mention Data mining functionality, classification, prediction, clustering and evolution analysis?
  4. What are the challenges in methodology of Data Mining technology?
  5. What defines a Data Mining Task Explain at least 5 primitives?
  6. What is knowledge discovery?
  7. What is a data? What do you mean by quality of data?
  8. What is data preprocessing?
  9. Explain the following
    1. Sampling
    2. Dimensionality reduction
    3. Discretization and binarization
  10. Explain the similarity and dissimilarity between 2 objects.
  11. What is Ecludian distance? Write the generalized Minkowski distance metric for various values r.
  12. What is simple matching coefficients and Jaccard coefficient? Explain with examples.
  13. What is meant by cousine similarity? Explain with example.
  14. What are the issues related to proximity measures?
  15. Discuss on selection on right proximity measures.
  16. What is KDD? Explain the different stages of KDD process.
  17. Explain the factors used for measuring the central tendency.
  1. Explain in brief about boxplot analysis.
  2. What is the impact of missing data? Explain how it is handle.
  3. What is noisy data? Explain how it is handle.
  4. Explain how binning method handles noisy data.
  5. Suppose that the values for the given set of data are grouped into intervals. the intervals and corresponding frequencies are as follows age frequency 1-5 200 5-15 450 15-20 300 20-50 1500 50-80 700 80-110 44 Compute approximate median value for the data.
  6. Suppose that the data for analysis includes the attribute age. The age values for the data tuples are (in increasing order) 13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30, 33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70.
    1. What is mean of data? What is median?
    2. What is the mode of the data? Comment on the datas modality (i.e., bimodal, trimodal, etc.).
    3. What is the midrange of the data?
    4. Give the five-number summary of the data.
    5. Show a boxplot of the data.
    6. Can you find (roughly) the first quartile (Q1) and the third quartile (Q3) of the data?
  7. Explain Univariate and Bivariate analysis.
  8. Suppose a group of 12 sales price records has been sorted as follows : 5, 10, 11, 13, 15, 35, 50, 55, 72, 92, 204, 215. Partitions into 3 bins by each of the following methods and also plot histogram :
    1. equal-frequency (equal-depth) partitioning
    2. equal-width partitioning
  9. Define different type of normalization method. Given the following data (in increasing order) for the attribute age: 13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30, 33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70. Answer the following:

Unit-2: Frequent Data Mining/ Association Rule Mining

  1. What is Apriori algorithm?
  2. Explain the association rule Mining?
  3. Explain the importance of frequent pattern mining
  4. State the relationship between frequent patterns and association rules
  5. Explain apriori base approach for mining frequent patterns.
  6. Explain in brief about apriori: candidate generation and test approach with an example.
  7. Construct the frequent pattern tree for the following database: TID Items Bought frequent items 100 {f, a, c, d, g, i, m, p} {f, c, a, m, p} 200 {a, b, c, f, l, m, o} {f, c, a, b, m} 300 {b, f, h, j, o, w} {f, b} 400 {b, c, k, s, p} {c, b, p} 500 {a, f, c, e, l, p, m, n} {f, c, a, m, p}
  8. Write short notes on Maximal Frequent Item Set & Closed Frequent Item Set.
  9. Explain in detail about support and Confidence Measures with an example.
  10. A database have five transactions. Let minSup = 60% and minConf = 80%.

TID Items Bought 100 {M, O, N, K, E, Y } 200 {D, O, N, K, E, Y } 300 {M, A, K, E} 400 {M, U, K, E, Y } 500 {C, O, O, K, I, E} Find all frequent itemsets using Apriori and FPGrowth respectively.

Unit-3: Classification & Clustering

  1. Define classification. Explain the purposes of using a classification model
  2. What is a decision tree? How a decision tree works?
  3. Explain the measures that can be used to determine the best way to split the record
  4. Explain the rule based classifier with an example
  5. Explain the Nearest-Neighbor classifier.Discus the k-nearest neighbor classification algorithm
  6. Give comparison between supervised learning and unsupervised learning.
  7. Give the outline of Bayesian classification.Give the outline of Nave Bayesian classification
  8. Explain in brief training Bayesian Belief Network.
  9. Explain in brief the IF THEN Rules for classification.
  10. Explain overfitting, underfitting with example.
  11. Given the training data in the table below (Buy Computer data), predict the class of the following new example using Naive Bays classification: age <= 30, income = medium, student = yes, credit-rating = fair RID age income student credit-rating Class:buy-computer 1 <= 30 high no fair no 2 <= 30 high no excellent no 3 31... 40 high no fair yes 4 > 40 medium no fair yes 5 > 40 low yes fair yes 6 > 40 low yes excellent no 7 31... 40 low yes execellent yes 8 <= 30 medium no fair no 9 <= 30 low yes fair yes 10 > 40 medium yes fair yes 11 <= 30 medium yes execellent yes 12 31... 40 medium no execellent yes 13 31... 40 high yes fair yes 14 > 40 medium no execellent no
  12. Given the training data from above question (Buy Computer Data), predict the class of the following new example using k-Nearest Neighbour for k = 5 age <= 30, income=medium, student=yes, credit-rating=fair
  1. Compute the Euclidean distance between two objects.
  2. Compute the Manhattan distance between two objects.
  3. Compute the Minkowski distance between two objects using q=3.
  4. Suppose that the data mining task is to cluster the following eight points (with (x,y) representing location) into tree cluster; A 1 (2, 10), A 2 (2, 5), A 3 (8, 4), B 1 (5, 8), B 2 (7, 5), B 3 (6, 4), C 1 (1, 2), C 2 (4, 9) The distance function is Euclidean distance. Suppose initially we design A 1 , B 1 , and C 1 as the center of each cluster respectively. Use K-Means clustering algorithm to show only
  5. The three cluster centers after the first round execution.
  6. The three final cluster. *****All The Best*****