Download CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE

CUSTOMER_CODE
SMUDE
DIVISION_CODE
SMUDE
EVENT_CODE
APR2016
ASSESSMENT_CODE MIT401_APR2016
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
11570
QUESTION_TEXT
List and explain the web content mining problems.
SCHEME OF EVALUATION There are 5 problems. Each explanation carries 2 Marks
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
11572
QUESTION_TEXT
Explain various characteristics of data warehouse?
1.Subject oriented
2.Integrated
SCHEME OF EVALUATION 3.Non Volatile
4.Time variant
(2.5 marks each)(10 marks)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
72809
QUESTION_TEXT
Explain binning methods for data smoothing. Give sorted data for
price (in dollars)=4, 8, 15, 21, 21, 24, 25, 28, 34.
SCHEME OF
EVALUATION
Partition into (equidepth) bins:
Bin 1: 4, 8, 15
Bin 2: 21, 21, 24
Bin 3: 25, 28, 34
Smoothing by bin means:
Bin 1: 9, 9, 9
Bin 2: 22, 22, 22
Bin 3: 29, 29, 29
Smoothing by bin boundaries:
Bin 1: 4, 4, 15
Bin 2: 21, 21, 24
Bin 3: 25, 25, 34 (10 marks)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
72811
QUESTION_TEXT
Explain major Source Data Components of Data Warehouse
Architecture.
SCHEME OF
EVALUATION
Source Data Component
a. Production Data: (2.5 marks)
This category of data comes from the various operational systems of
the enterprise. The significant and disturbing characteristic of
production data is disparity. Great challenge is to standardize and
transform the disparate data from the various production systems,
convert the data, and integrate the pieces into useful data for storage in
the Data Warehouse.
b. Internal Data: (2.5 marks)
In every organization, users keep their “private” spreadsheets,
documents, customer profiles, and sometimes even departmental
databases. This is the internal data, parts of which could be useful for
Data Warehouse for analysis. Internal data adds additional complexity
to the process of transforming and integrating the data before it can be
stored in the Data Warehouse.
c. Archived Data: (2.5 marks)
Operational systems are primarily intended to run the current business.
In every operational system, periodically take the old data and store it
in archived files. The circumstances in organization dictate how often
and which portions of the operational databases are archived for
storage. Some data is archived after a year.
d. External Data: (2.5 marks)
Most executives depend on data from external sources for a high
percentage of the information they use. They use statistics relating to
their industry produced by external agencies. They use market share
data of competitors. They use standard values of financial indicators for
their business to check on their performance.
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
SCHEME OF
EVALUATION 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
(8 marks)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
126115
Explain the categories of web mining.
QUESTION_TEXT
SCHEME OF
EVALUATION
Web mining can be broadly divided into three catagories.
a)Web content mining.
b)Web structure mining.
c)Web usage mining.
a)Web content mining : web content mining targets the knowledge discovery in which
the main objects are the traditional collections of multimedia document such as images
, video and audio which are embedded in or linked to the web pages. Web content
mining could be differentiated from two points of view: Agent based approach or
database approach. The first approach aims on improving the information finding and
filtering. The second approach aims on modeling the data on web into more structured
form in order to apply standard database querying mechanism and datamining
application analyze it. Web content mining problems and challenges are
data/information extraction , web information integration, opinion extraction from
online sources,knowledge synthesis, segmenting web pages and detecting noise .
b) Web structure mining: this focuses on analysis of the link structure of the web and
one of its purpose is to identify more preferable documents. The different objets are
linked in some way. The appropriate handling f the links could lead to potential
correlations and then improve the predictive accuracy of the learned models. The goal
of the wb structure mining is to generate structural summary about the web site and
web page. Based on the topology web structure mining will categorize the web pages
and generate the information such as the similarity and relationship between different
web sites. Web structure mining can also have another direction discovering the
structure of web document itself. This type of structure mining can be used to reveal the
structure of web pages.
c) Web usage mining: this focuses on the techniques that could predict the behavior of
users while they are interacting with the WWW. Web usage mining discover user
navigation patterns from web data tries to discover the use full information from the
secondary data derived from the interactions of the users while surfing on the web. Web
usage mining collects the data from web log records to discover user access patterns of
web pages .The insight knowledge could be utilized in personalization, system
improvement, site modification, business intelligence and usage characterization. In
general there are mainly 4 kinds of data mining techniques applied to the web mining
domain to discover the user navigation pattern: Association rue mining, sequential
pattern mining, clustering, classification.