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PROGRAM
Master of Science in Information Technology(MSc IT)
SEMESTER
FOURTH
SUBJECT CODE & NAME
MIT401– Data Warehousing and Data Mining
Qus:1 Define data warehouse. Differentiate between
OLTP and data warehouse.
Answer: Data
Warehouses and Data Warehouse applications are designed primarily to support
executives, senior managers, and business analysts in making complex business
decisions. Data Warehouse applications provide the business community with
access to accurate consolidated information from various internal and external
sources. The goal of using a Data Warehouses to have an efficient way of
managing information and analyzing data. Now day’s
Qus:2 What is schema? List the name of data
warehouse schemas and explain Snowflake schema.
Answer: A
schema is a collection of database objects, including tables, views, indexes,
and synonyms. There are three schemas existed to define Data Warehouse,
Star Schema
Snowflake Schema
Fact-
Qus:3 What is Metadata? Explain technical and
operational metadata.
Answer: Since
the early 1990s, data warehouses have been at the forefront of information
technology applications as a way for organizations to effectively use digital
information for business planning and decision-making. Data warehouses are
computer based information
Qus:4 Explain data mining and knowledge discovery
process.
Answer: Data
Mining is not specific to any industry – it requires intelligent technologies
and the willingness to explore the possibility of hidden knowledge that resides
in the data. Data Mining is also referred to
Qus:5 Briefly describe K-means method for
clustering. List its advantages and drawbacks.
Answer: K-means
(Mac Queen, 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 is to define k cancroids, one
for each cluster.
The
basic step of k-means clustering is simple. In the beginning, we determine
number of cluster K and we
Qus: 6 Explain how Data Mining is useful for
following:
(a) Targeted marketing.
(b) Telecommunications.
Answer: (a) Targeted marketing.
Insurance
and direct mail are two industries that rely on data analysis to make
profitable business decisions. For example, insurers must be able to accurately
assess the risks posed by their policyholders to set insurance premiums at
competitive levels. For example, overcharging low
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