Get
fully solved assignment. Buy online from website
online
store
or
plz
drop a mail with your sub code
we
will revert you within 2-3 hour or immediate
Charges
rs 125/subject
PROGRAM
Master of Science in Information
Technology (MSc IT)Revised Fall 2011
SEMESTER
4
SUBJECT CODE & NAME
MIT401– Data Warehousing and Data
Mining
Qus:1 (a)Explain the Key Issues
during data warehouse construction.
(b) Differentiate between OLTP
database and Data Warehouse database.
Answer: Planning for your Data Warehouse
begins with a thorough consideration of the key issues. Answers to the key
questions are vital for the proper planning and the successful completion of
the project. Therefore, let us consider the pertinent issues, one by one.
Values and Expectations.
Qus:2 Describe
the functionalities and advantages of Data Warehouses.
Answer: A Data Warehouse provides a common
data model for data, regardless of the data source. This makes it easier to
report and analyze information than it would be if multiple data models from
disparate sources were used to retrieve information such as sales invoices,
order receipts, general ledger charges, etc.
Prior
Qus:3 Explain how E-R modeling
differs from Dimensional modeling.
Answer: E-R model represents business
processes within the enterprise and serves as a blueprint for operational
database system(s) whereas Dimensional Model represents subject areas within
the enterprise and serves as a blueprint for analytical system(s).
The key
Qus:4 Describe the strategies for
data reduction.
Answer: Data reduction techniques can be
applied to obtain a reduced representation of the data set that is much smaller
in volume, yet closely maintains the integrity of the original data. That is,
mining on the reduced data set should be more efficient yet produce the same
(or almost the same) analytical results.
Qus:5 Explain K-means method for
clustering. Write its advantages and disadvantages.
Answer: K-means (MacQueen, 1967) is one of
the simplest unsupervised learning algorithms that solve the well known
clustering problem. The procedure follows a simple and easy way to classify a
given data set through a certain number of clusters (assume k clusters) fixed a
priori. The main idea
Qus:6 Describe how Data Mining is
useful in telecommunications industry?
Answer: The data mining applications in
telecommunications industry, and a learning system for decision support in
telecommunications case study, knowledge processing in control systems, and
aircraft control case study are discussed in this section. A few scenarios
where data mining may improve telecommunication services are discussed. The
deregulation of the telecommunications
Get
fully solved assignment. Buy online from website
online
store
or
plz
drop a mail with your sub code
we
will revert you within 2-3 hour or immediate
Charges
rs 125/subject
No comments:
Post a Comment