Download CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE

CUSTOMER_CODE
SMUDE
DIVISION_CODE
SMUDE
EVENT_CODE
APR2016
ASSESSMENT_CODE MCA5043_APR2016
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
11567
QUESTION_TEXT
List the objectives of data mining in telecommunication.
SCHEME OF
EVALUATION
1.Helps to understand the business involved, identify
telecommunication patterns, catch fraudulent activities, make better use
of resources and improve the quality of service.
2.Algorithms include CART, c4.5, neural networks and Bayesian
classifiers among others.
3.The ability to handle noise in this case is obviously critical to the
successful application of data mining algorithms.
4.The company’s face the problem of churning.
5.Data mining is one solution to do appropriate credit scoring and to
combat churns in the telecom industry.
6.Used to churn analysis to perform 2 key tasks: Predict and
Understand.
7.Decision support in telecommunication forms the rules that can be
used as decision support rules.
8.In central system RTKP procedure based on conjunctive and
disjunctive matrices and operators.
9.KDD has delivered a variety technique to discover patterns from vast
amount of data which helps in mining for complex data.
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
11571
QUESTION_TEXT
What are the objectives of using data mining in business?
Explain.
SCHEME OF
EVALUATION
There are 8 objectives. Each carries 1.25 Marks
QUESTION_T
DESCRIPTIVE_QUESTION
YPE
QUESTION_ID 72814
QUESTION_T
Define Data Mining. Differentiate between Data Mining and DBMS.
EXT
Data Mining: It is the search for the relationships and global patterns that
exist in large databases but are hidden among vast amounts of data, such as
relationship between patient data and their medical diagnosis. It is the
process of discovering meaningful, new correlation patterns and trends by
sifting through large amounts of stored in repositories, using pattern
recognition techniques. (2 marks)
DBMS VS Data Mining
SCHEME OF
EVALUATION
(8 marks)
QUESTION_T
DESCRIPTIVE_QUESTION
YPE
QUESTION_I
126111
D
QUESTION_T
Explain the basic tasks involved in Data transformation.
EXT
Selection : This takes place at the beginning of the whole process of data
transformation. You select either the whole records or parts of several
SCHEME OF
records from the source systems. The task of selection usually forms part
EVALUATIO
of the extraction function itself
–2 Marks
N
Splitting/Joining : This task includes the types of data manipulation you need
to perform on the selected parts of source records. Sometimes you will be
splitting the selected parts even further during data transformation. Joining
of parts selected from many source systems is more widespread in the
Data Warehouse
environment
2
Marks
Conversion : This is an all–inclusive task. It includes a large variety of
rudimentary conversions of single fields for two primary reasons – one to
standardize among the data extraction from disparate source systems, and
the other to make the fields usable and understandable to the
users
2 Marks
Summarization : Sometimes you may find that it is not feasible to keep data at
the lowest level of detail in your Data Warehouse. It may be that none of
your users ever need data at the lowest granularity for analysis or
querying
2 Marks
Enrichment : This task is the rearrangement and simplification of individual
fields to make them more useful for the Data Warehouse environment.
You may use one or more fields from the same input record to create a
better view of the data for the Data Warehouse. This principle is extended
when one or more fields originate from multiple records, resulting in a
single field for the Data
Warehouse
2 Marks
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
126113
QUESTION_TEXT
Briefly explain any TWO scientific applications using data
mining
SCHEME OF
EVALUATION
a.
b.
c.
Biomedical engineering
Telecommunications
Climate data and the earth’s ecosystems
(Any 2 from the above which carries 5 marks each)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
126114
QUESTION_TEXT
Discuss the following data warehouse schema
a. Star schema
b.
SCHEME OF EVALUATION a.
Snowflake schema
Star schema (5 marks)
b.
Snowflake schema (5 marks)