STUPIDSID
1(A)
What is dimensional modelling ? Design the data warehouse for wholesale furniture Company. The data warehouse has to allow analysing the company's situation at least with respect to the Furniture, Customer and Time. More ever, the company customer needs to analyse: The furniture with respect to its type category and material. The customers with respect to their spatial location, by considering at least cities, regions and states. The company is interested is learning the quantity, income and discount of its sales.
10 M
1(A)
What is dimensional modeling? Design the data warehouse for wholesale furniture Company. The data warehouse has to allow analysing the company's situation at least with respect to the Furniture , Customer and Time. More ever, the company needs to analyse: The furniture with repect to its type category and material. The cutomer with respect to their spatial location, by considering at least citites, regions and states. The company is interested in learning the quantity, income and discount of its sales.
10 M
1(B)
Discuss different steps involved in Data Pre-processing
10 M
1(B)
Discuss the different steps involved in Data Pre-processing.
10 M
2(A)
The college wants to record the Marks for the courses completed by students using the dimesions: i) Course , ii) Student, iii) Time & measure Aggregate marks Create a cube describe following OLAP operations.
i) Slice ii) Dice iii) Roll up iv) Drill down v) Pivot
i) Slice ii) Dice iii) Roll up iv) Drill down v) Pivot
10 M
2(A)
The college wants to record the Marks fot the courses completed by students using the dimensions : i) Course, ii) Student, iii) Time & measure Aggregate marks Create a Cube and describe following OLAP operations.
i)Slice ii) Dice iii) Roll up iv) Drill down v) Pivot
i)Slice ii) Dice iii) Roll up iv) Drill down v) Pivot
10 M
2(B)
Apply the Naive Bayes classifier algorithm for buys computer classification and classify the tuple X= (age - "young", income=" medium", student=" yes" and credit - rating =" fair")
| Id | Age | Income | Student | Credit- rating | buys computer |
| 1 | young | high | no | fair | no |
| 2 | young | high | no | good | no |
| 3 | middle | high | no | fair | yes |
| 4 | old | medium | no | fair | yes |
| 5 | old | low | yes | fair | yes |
| 6 | old | low | yes | good | no |
| 7 | middle | low | yes | good | yes |
| 8 | young | medium | no | fair | no |
| 9 | young | low | yes | fair | yes |
| 10 | old | medium | yes | fair | yes |
| 11 | young | medium | yes | good | yes |
| 12 | middle | medium | no | good | yes |
| 13 | middle | high | yes | fair | yes |
| 14 | old | medium | no | good | no |
10 M
2(B)
Apply the Naive Bayes classifier algorithm for buys computer classification and classify the tuple = X(age "young". Income = "medium", student = "yes" and credit - rating = "fair")
| ID | Age | Income | Student | Credit-rating | buys computer |
| 1 | young | high | no | fair | no |
| 2 | young | high | no | good | no |
| 3 | middle | high | no | fair | yes |
| 4 | old | medium | no | fair | yes |
| 5 | old | medium | no | fair | yes |
| 6 | old | low | yes | good | no |
| 7 | middle | low | yes | good | yes |
| 8 | young | medium | no | fair | yes |
| 9 | young | low | yes | fair | yes |
| 10 | old | medium | yes | fair | yes |
| 11 | young | medium | yes | fair | yes |
| 12 | middle | medium | no | good | yes |
| 13 | middle | high | yes | fair | yes |
| 14 | old | medium | no | good | no |
10 M
3(A)
Explain ETL of data warehousing in details?
10 M
3(A)
Explain ETL of data warehousing in details?
10 M
3(B)
Explain types of attributes and data visualization for data exploration
10 M
3(B)
Explain types of attributes and data visualization for data exploration.
10 M
4(A)
Illustrate the architecture of Data Warehouse system. Differentiate Data warehouse and Data Mart.
10 M
4(A)
Illustrate the architecture of Data Warehouse system. Differentiate Data warehouse and Data Mart.
10 M
4(B)
Explain K-means clustering algorithm? Apply K-Means algorithm for the following Data Set = { 15, 15, 16, 19, 20, 21, 22, 28, 35, 40, 41, 42, 43, 44, 60, 61, 65}
10 M
4(B)
Explain K- Means clustering algorithm? Apply K-mean algortihm for the following Data set with two clusters. Data Set= {15, 15, 16, 19,19, 20, 20, 21, 22, 28, 35,40, 41, 42, 43, 44, 69, 61,65}
10 M
5(A)
Explain Updates to dimension tables in detail.
10 M
5(A)
Explain Updates to dimensions table in detail.
10 M
5(B)
A database has ten transactions. Let minmum support = 30% and minimum Cofidence = 70%
i) Find all frequent patterns using AprioriAlgorithm.
ii) List strong association rules.
i) Find all frequent patterns using AprioriAlgorithm.
ii) List strong association rules.
| Transaction_Id | Items |
| 01 | A, B, C, D |
| 02 | A, B, C, D, E, G |
| 03 | A, C, G, H, K |
| 04 | B,C, D, E, K |
| 05 | D, E, F, H, L |
| 06 | A, B, C, D, L |
| 07 | B, I, E, K, L |
| 08 | A, B, D, E, K |
| 09 | A, E, F, H, L |
| 010 | B, C, D, F |
10 M
5(B)
A database has ten transactions. Let minimum support = 30% and minimum.
Confidence =70%
i) Find all frequent patterns using Apriori Algorithm.
ii) List strong association rules.
Confidence =70%
i) Find all frequent patterns using Apriori Algorithm.
ii) List strong association rules.
| Transaction_Id | Items |
| 01 | A, B,C, D |
| 02 | A,B,C,D,E,G |
| 03 | A,C,G,H,K |
| 04 | B,C,D, E,K |
| 05 | D,E,F,H,L |
| 06 | A,B,C,D,L |
| 07 | B,I,E,K,L |
| 08 | A,B,D,E,K |
| 09 | A,E,F,H,L |
| 10 | B,C,D,F |
10 M
Write short note any four question from Q.6(a, b, c, d, e,)
6(a)
Major-issues in Data Mining
5 M
Write short note any four question from Q.6(a, b, c, d, e)
6(a)
Major issues in Data Mining
5 M
6(b)
Metadata in Data Warehouse
5 M
6(b)
Metadata in Data Warehouse
5 M
6(c)
FP Tree
5 M
6(c)
FP Tree
5 M
6(d)
DBSCAN
5 M
6(d)
DBSCAN
5 M
6(e)
Hierarchical Clustering
5 M
6(e)
Hirerarchical Clustering
5 M
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