Скачать или смотреть BE -COMP | Data Mining and Warehousing (DMW) | Solved In-Sem 2018 Question Paper | SPPU

BE -COMP | Data Mining and Warehousing (DMW) | Solved In-Sem 2018 Question Paper | SPPU


BE/Insem./Oct.
-789

B.E. (Computer Engineering)
DATA MINING AND WAREHOUSING
(2017 Pattern) (Elective - I) (Semester - I)
Time : 1 Hour] [Max. Marks : 30
INSTRUGTIONS to the GANDIDATES:
1) 5olve Q.1 or Q.2, Q.3 or Q.4, Q.5 or Q.6.
2) Assume suitable data, if NEGESSARY.
3) Neat diagrams must be drawn wherever NEGESSARY. 4) Figures to the right INDIGATE full marks.

Ql) a) Suppose that the minimum and maximum values for the attribute income are $l2,000 and $98,000 respectively. Normalize income value $73,600 to the range [0.0, l.0] using min-max normalization method. [4]
b) Explain various data cleaning techniques. [4]
c) What is correlation analysis? [2]
OR
Q2) a) Explain different methods for attribute subset selection (any 2). [4]
b) For the given attribute marks values : [4]
35, 45, 50, 55, 60, 65, 75
Compute mean, median, mode.
Also compute Five number summary of above data.
c) Enlist different methods of sampling. [2]

QS) a) From the architectural point of view, explain different data warehouse models. [4]
b) Differentiate between ROLAP, MOLAP and HOLAP. [4]
c) What is Concept Hierarchy? Explain. [2]
OR
Q4) a) Draw and Explain a data warehouse architecture. [4]
b) Explain following OLAP operations with example. [4]
i) Drill Up
ii) Slice & Dice
c) What is fact table and dimension table. [2]

P.T.O.

Q5) a) Calculate Euclidean and Manhattan distance between following two objects. [4]
A = {2, 4, 8, 6,}, B = {3, 4, 6, 7}
b) How to compute dissimilarity between categorical variables. Explain with suitable example. [4]
c) What is cosine similarity? [2]
OR
Q6) a) Compute cosine similarity among following documents using term frequency vector [4]
d1 : "The sun in the sky is bright"
d2 : "We can see the shining sun, the bright sun"
b) How to compute dissimilarity between ordinal variables. Explain with suitable example. [4]
c) Explain Data matrix and Dissimilarity matrix. [2]