CS1004:
DATA WAREHOUSING AND MINING
TWO
MARKS QUESTIONS AND ANSWERS
Unit
I
1.Define
Data mining.
It
refers to extracting or “mining” knowledge from large amount of data. Data
mining
is a process of discovering interesting knowledge from large amounts of data
stored
either, in database, data warehouse, or other information repositories
2.Give
some alternative terms for data mining.
• Knowledge mining
• Knowledge extraction
• Data/pattern analysis.
• Data Archaeology
• Data dredging
3.What
is KDD.
KDD-Knowledge
Discovery in Databases.
4.What
are the steps involved in KDD process.
• Data cleaning
• Data Mining
• Pattern Evaluation
• Knowledge Presentation
• Data Integration
• Data Selection
• Data Transformation
5.What
is the use of the knowledge base?
Knowledge
base is domain knowledge that is used to guide search or evaluate the
interestingness
of resulting pattern. Such knowledge can include concept hierarchies used
to
organize attribute /attribute values in to different levels of abstraction.
Data
Mining
6.Arcitecture
of a typical data mining system.
Knowledge
base
7.Mention
some of the data mining techniques.
• Statistics
• Machine learning
• Decision Tree
• Hidden markov models
• Artificial Intelligence
• Genetic Algorithm
• Meta learning
8.Give
few statistical techniques.
• Point Estimation
• Data Summarization
• Bayesian Techniques
• Testing Hypothesis
• Correlation
• Regression
9.What
is meta learning.
Concept
of combining the predictions made from multiple models of data
mining
and analyzing those predictions to formulate a new and previously unknown
prediction.
GUI
Pattern
Evaluation
Database
or Data warehouse
server
DB
DW
10.Define
Genetic algorithm.
• Search algorithm.
• Enables us to locate optimal binary string by processing an
initial
random
population of binary strings by performing operations such as
artificial
mutation , crossover and selection.
11.What
is the purpose of Data mining Technique?
It
provides a way to use various data mining tasks.
12.Define
Predictive model.
It
is used to predict the values of data by making use of known results from a
different
set of sample data.
13.Data
mining tasks that are belongs to predictive model
• Classification
• Regression
• Time series analysis
•
14.Define
descriptive model
• It is used to determine the patterns and relationships in a sample
data. Data
mining
tasks that belongs to descriptive model:
• Clustering
• Summarization
• Association rules
• Sequence discovery
15.
Define the term summarization
The
summarization of a large chunk of data contained in a web page or a
document.
Summarization
= caharcterization=generalization
16.
List out the advanced database systems.
• Extended-relational databases
• Object-oriented databases
• Deductive databases
• Spatial databases
• Temporal databases
• Multimedia databases
• Active databases
• Scientific databases
• Knowledge databases
17.
Define cluster analysis
Cluster
analyses data objects without consulting a known class label. The class
labels
are not present in the training data simply because they are not known to begin
with.
18.Classifications
of Data mining systems.
• Based on the kinds of databases mined:
o According to model
_
Relational mining system
_
Transactional mining system
_
Object-oriented mining system
_
Object-Relational mining system
_
Data warehouse mining system
o Types of Data
_
Spatial data mining system
_
Time series data mining system
_
Text data mining system
_
Multimedia data mining system
• Based on kinds of Knowledge mined
o According to functionalities
_
Characterization
_
Discrimination
_
Association
_
Classification
_
Clustering
_
Outlier analysis
_
Evolution analysis
o According to levels of abstraction of the knowledge mined
_
Generalized knowledge (High level of
abstraction)
_
Primitive-level knowledge (Raw data level)
o According to mine data regularities versus mine data
irregularities
• Based on kinds of techniques utilized
o According to user interaction
_
Autonomous systems
_
Interactive exploratory system
_
Query-driven systems
o According to methods of data analysis
_
Database-oriented
_
Data warehouse-oriented
_
Machine learning
_
Statistics
_
Visualization
_
Pattern recognition
_
Neural networks
• Based on applications adopted
o Finance
o Telecommunication
o DNA
o Stock markets
o E-mail and so on
o
19.Describe challenges to
data mining regarding data mining methodology and user
interaction
issues.
• Mining different kinds of knowledge in databases
• Interactive mining of knowledge at multiple levels of abstraction
• Incorporation of background knowledge
• Data mining query languages and ad hoc data mining
• Presentation and visualization of data mining results
• Handling noisy or incomplete data
• Pattern evaluation
20.Describe challenges to
data mining regarding performance issues.
• Efficiency and scalability of data mining algorithms
• Parallel, distributed, and incremental mining algorithms
21.Describe issues
relating to the diversity of database types.
• Handling of relational and complex types of data
• Mining information from heterogeneous databases and global
information
systems
22.What
is meant by pattern?
Pattern
represents knowledge if it is easily understood by humans; valid on test
data
with some degree of certainty; and potentially useful, novel,or validates a
hunch
about
which the used was curious. Measures of pattern interestingness, either
objective or
subjective,
can be used to guide the discovery process.
23.How
is a data warehouse different from a database?
Data
warehouse is a repository of multiple heterogeneous data sources, organized
under
a unified schema at a single site in order to facilitate management
decision-making.
Database
consists of a collection of interrelated data.
UNIT
II
1.
Define Association Rule Mining.
Association
rule mining searches for interesting relationships among items in
a
given data set
2.
When we can say the association rules are interesting?
Association
rules are considered interesting if they satisfy both a minimum
support
threshold and a minimum confidence threshold. Users or domain experts
can
set such thresholds.
3.
Explain Association rule in mathematical notations.
Let
I-{i1,i2,…..,im} be a set of items
Let
D, the task relevant data be a set of database transaction T is a set of
items
An
association rule is an implication of the form A=>B where A C I, B C I,
and
An B=f. The rule
A=>B contains in the transaction set D with support s,
where
s is the percentage of transactions in D that contain AUB. The Rule A=> B
has
confidence c in the transaction set D if c is the percentage of transactions in
D
containing
A that also contain B.
4.
Define support and confidence in Association rule mining.
Support
S is the percentage of transactions in D that contain AUB.
Confidence
c is the percentage of transactions in D containing A that also contain
B.
Support
( A=>B)= P(AUB)
Confidence
(A=>B)=P(B/A)
5.
How are association rules mined from large databases?
• I step: Find all frequent item sets:
• II step: Generate strong association rules from frequent item sets
6.
Describe the different classifications of Association rule mining.
• Based on types of values handled in the Rule
i.
Boolean association rule
ii.
Quantitative association rule
• Based on the dimensions of data involved
i.
Single dimensional association rule
ii.
Multidimensional association rule
• Based on the levels of abstraction involved
i.
Multilevel association rule
ii.
Single level association rule
• Based on various extensions
i.
Correlation analysis
ii.
Mining max patterns
7.
What is the purpose of Apriori Algorithm?
Apriori
algorithm is an influential algorithm for mining frequent item sets for
Boolean
association rules. The name of the algorithm is based on the fact that the
algorithm
uses prior knowledge of frequent item set properties.
8.
Define anti-monotone property.
If
a set cannot pass a test, all of its supersets will fail the same test as well.
9.
How to generate association rules from frequent item sets?
Association
rules can be generated as follows
For
each frequent item set1, generate all non empty subsets of 1.
For
every non empty subsets s of 1, output the rule “S=>(1-s)”if
Support
count(1)
=min_conf,
Support_count(s)
Where
min_conf is the minimum confidence threshold.
10.
Give few techniques to improve the efficiency of Apriori algorithm.
• Hash based technique
• Transaction Reduction
• Portioning
• Sampling
• Dynamic item counting
11.
What are the things suffering the performance of Apriori candidate
generation
technique.
• Need to generate a huge number of candidate sets
• Need to repeatedly scan the scan the database and check a large
set of
candidates
by pattern matching
12.
Describe the method of generating frequent item sets without candidate
generation.
Frequent-pattern
growth(or FP Growth) adopts divide-and-conquer
strategy.
Steps:
Compress
the database representing frequent items into a frequent pattern tree
or
FP tree
Divide
the compressed database into a set of conditional database
Mine
each conditional database separately
13.
Define Iceberg query.
It
computes an aggregate function over an attribute or set of attributes in
order
to find aggregate values above some specified threshold.
Given
relation R with attributes a1,a2,…..,an and b, and an aggregate function,
agg_f,
an iceberg query is the form
Select
R.a1,R.a2,…..R.an,agg_f(R,b)
From
relation R
Group
by R.a1,R.a2,….,R.an
Having
agg_f(R.b)>=threhold
14.
Mention few approaches to mining Multilevel Association Rules
• Uniform minimum support for all levels(or uniform support)
• Using reduced minimum support at lower levels(or reduced support)
• Level-by-level independent
• Level-cross filtering by single item
• Level-cross filtering by k-item set
15.
What are multidimensional association rules?
Association
rules that involve two or more dimensions or predicates
• Interdimension association rule: Multidimensional association rule
with no
repeated
predicate or dimension
• Hybrid-dimension association rule: Multidimensional association
rule with
multiple
occurrences of some predicates or dimensions.
16.
Define constraint-Based Association Mining.
Mining
is performed under the guidance of various kinds of constraints
provided
by the user.
The
constraints include the following
• Knowledge type constraints
• Data constraints
• Dimension/level constraints
• Interestingness constraints
• Rule constraints.
17.
Define the concept of classification.
Two
step process
• A model is built describing a predefined set of data classes or
concepts.
The
model is constructed by analyzing database tuples described by
attributes.
• The model is used for classification.
18.
What is Decision tree?
A
decision tree is a flow chart like tree structures, where each internal
node
denotes a test on an attribute, each branch represents an outcome of the test,
and
leaf nodes represent classes or class distributions. The top most in a tree is
the
root
node.
19.
What is Attribute Selection Measure?
The
information Gain measure is used to select the test attribute at each node
in
the decision tree. Such a measure is referred to as an attribute selection
measure
or
a measure of the goodness of split.
20.
Describe Tree pruning methods.
When
a decision tree is built, many of the branches will reflect anomalies in
the
training data due to noise or outlier. Tree pruning methods address this
problem
of over fitting the data.
Approaches:
• Pre pruning
• Post pruning
21.
Define Pre Pruning
A
tree is pruned by halting its construction early. Upon halting, the node
becomes
a leaf. The leaf may hold the most frequent class among the subset
samples.
22.
Define Post Pruning.
Post
pruning removes branches from a “Fully grown” tree. A tree node is
pruned
by removing its branches.
Eg:
Cost Complexity Algorithm
23.
What is meant by Pattern?
Pattern
represents the knowledge.
24.
Define the concept of prediction.
Prediction
can be viewed as the construction and use of a model to assess the
class
of an unlabeled sample or to assess the value or value ranges of an attribute
that
a given sample is likely to have.
Unit
III
1.Define
Clustering?
Clustering
is a process of grouping the physical or conceptual data object into
clusters.
2.
What do you mean by Cluster Analysis?
A
cluster analysis is the process of analyzing the various clusters to organize
the
different
objects into meaningful and descriptive objects.
3.
What are the fields in which clustering techniques are used?
• Clustering is used in biology to develop new plants and animal
taxonomies.
• Clustering is used in business to enable marketers to develop new
distinct
groups of their customers and characterize the customer group on basis
of
purchasing.
• Clustering is used in the identification of groups of automobiles
Insurance
policy customer.
• Clustering is used in the identification of groups of house in a
city on
the
basis of house type, their cost and geographical location.
• Clustering is used to classify the document on the web for
information
discovery.
4.What
are the requirements of cluster analysis?
The
basic requirements of cluster analysis are
• Dealing with different types of attributes.
• Dealing with noisy data.
• Constraints on clustering.
• Dealing with arbitrary shapes.
• High dimensionality
• Ordering of input data
• Interpretability and usability
• Determining input parameter and
• Scalability
5.What
are the different types of data used for cluster analysis?
The
different types of data used for cluster analysis are interval scaled, binary,
nominal,
ordinal and ratio scaled data.
6.
What are interval scaled variables?
Interval
scaled variables are continuous measurements of linear scale.
For
example, height and weight, weather temperature or coordinates for any cluster.
These
measurements can be calculated using Euclidean distance or Minkowski distance.
7.
Define Binary variables? And what are the two types of binary variables?
Binary
variables are understood by two states 0 and 1, when state is 0, variable is
absent
and when state is 1, variable is present. There are two types of binary
variables,
symmetric
and asymmetric binary variables. Symmetric variables are those variables that
have
same state values and weights. Asymmetric variables are those variables that
have
not
same state values and weights.
8.
Define nominal, ordinal and ratio scaled variables?
A
nominal variable is a generalization of the binary variable. Nominal variable
has
more than two states, For example, a nominal variable, color consists of four
states,
red,
green, yellow, or black. In Nominal variables the total number of states is N
and it is
denoted
by letters, symbols or integers.
An
ordinal variable also has more than two states but all these states are ordered
in
a meaningful sequence.
A
ratio scaled variable makes positive measurements on a non-linear scale, such
as
exponential scale, using the formula
AeBt or Ae-Bt
Where
A and B are constants.
9.
What do u mean by partitioning method?
In
partitioning method a partitioning algorithm arranges all the objects into
various
partitions, where the total number of partitions is less than the total number
of
objects.
Here each partition represents a cluster. The two types of partitioning method are
k-means
and k-medoids.
10.
Define CLARA and CLARANS?
Clustering
in LARge Applications is called as CLARA. The efficiency of
CLARA
depends upon the size of the representative data set. CLARA does not work
properly
if any representative data set from the selected representative data sets does
not
find
best k-medoids.
To
recover this drawback a new algorithm, Clustering Large Applications based
upon
RANdomized search (CLARANS) is introduced. The CLARANS works like
CLARA,
the only difference between CLARA and CLARANS is the clustering process
that
is done after selecting the representative data sets.
11.
What is Hierarchical method?
Hierarchical
method groups all the objects into a tree of clusters that are arranged
in
a hierarchical order. This method works on bottom-up or top-down approaches.
12.
Differentiate Agglomerative and Divisive Hierarchical Clustering?
Agglomerative
Hierarchical clustering method works on the bottom-up approach.
In
Agglomerative hierarchical method, each object creates its own clusters. The
single
Clusters
are merged to make larger clusters and the process of merging continues until
all
the
singular clusters are merged into one big cluster that consists of all the
objects.
Divisive
Hierarchical clustering method works on the top-down approach. In this
method
all the objects are arranged within a big singular cluster and the large
cluster is
continuously
divided into smaller clusters until each cluster has a single object.
13.
What is CURE?
Clustering
Using Representatives is called as CURE. The clustering algorithms
generally
work on spherical and similar size clusters. CURE overcomes the problem of
spherical
and similar size cluster and is more robust with respect to outliers.
14.
Define Chameleon method?
Chameleon
is another hierarchical clustering method that uses dynamic modeling.
Chameleon
is introduced to recover the drawbacks of CURE method. In this method two
clusters
are merged, if the interconnectivity between two clusters is greater than the
interconnectivity
between the objects within a cluster.
15.
Define Density based method?
Density
based method deals with arbitrary shaped clusters. In density-based
method,
clusters are formed on the basis of the region where the density of the objects
is
high.
16.
What is a DBSCAN?
Density
Based Spatial Clustering of Application Noise is called as DBSCAN.
DBSCAN
is a density based clustering method that converts the high-density objects
regions
into clusters with arbitrary shapes and sizes. DBSCAN defines the cluster as a
maximal
set of density connected points.
17.
What do you mean by Grid Based Method?
In
this method objects are represented by the multi resolution grid data
structure.
All
the objects are quantized into a finite number of cells and the collection of
cells build
the
grid structure of objects. The clustering operations are performed on that grid
structure.
This method is widely used because its processing time is very fast and that is
independent
of number of objects.
18.
What is a STING?
Statistical
Information Grid is called as STING; it is a grid based multi resolution
clustering
method. In STING method, all the objects are contained into rectangular cells,
these
cells are kept into various levels of resolutions and these levels are arranged
in a
hierarchical
structure.
19.
Define Wave Cluster?
It
is a grid based multi resolution clustering method. In this method all the
objects
are
represented by a multidimensional grid structure and a wavelet transformation
is
applied
for finding the dense region. Each grid cell contains the information of the
group
of
objects that map into a cell. A wavelet transformation is a process of
signaling that
produces
the signal of various frequency sub bands.
20.
What is Model based method?
For
optimizing a fit between a given data set and a mathematical model based
methods
are used. This method uses an assumption that the data are distributed by
probability
distributions. There are two basic approaches in this method that are
1.
Statistical Approach
2.
Neural Network Approach.
21.
What is the use of Regression?
Regression
can be used to solve the classification problems but it can also be used
for
applications such as forecasting. Regression can be performed using many
different
types
of techniques; in actually regression takes a set of data and fits the data to
a
formula.
22.
What are the reasons for not using the linear regression model to estimate the
output
data?
There
are many reasons for that, One is that the data do not fit a linear model, It
is
possible
however that the data generally do actually represent a linear model, but the
linear
model generated is poor because noise or outliers exist in the data.
Noise
is erroneous data and outliers are data values that are exceptions to the usual
and
expected
data.
23.
What are the two approaches used by regression to perform classification?
Regression
can be used to perform classification using the following approaches
1.
Division: The data are divided into regions
based on class.
2.
Prediction: Formulas are
generated to predict the output class value.
24.
What do u mean by logistic regression?
Instead
of fitting a data into a straight line logistic regression uses a logistic
curve.
The
formula for the univariate logistic curve is
P=
e (C0+C1X1)
1+e
(C0+C1X1)
The
logistic curve gives a value between 0 and 1 so it can be interpreted as the
probability
of class membership.
25.
What is Time Series Analysis?
A
time series is a set of attribute values over a period of time. Time Series
Analysis
may be viewed as finding patterns in the data and predicting future values.
26.
What are the various detected patterns?
Detected
patterns may include:
¨ Trends : It may be viewed
as systematic non-repetitive changes to the values over
time.
¨ Cycles : The observed
behavior is cyclic.
¨ Seasonal : The detected
patterns may be based on time of year or month or day.
¨ Outliers : To assist in
pattern detection , techniques may be needed to remove or
reduce
the impact of outliers.
27.
What is Smoothing?
Smoothing
is an approach that is used to remove the nonsystematic behaviors
found
in time series. It usually takes the form of finding moving averages of
attribute
values.
It is used to filter out noise and outliers.
28.
Give the formula for Pearson’s r
One
standard formula to measure correlation is the correlation coefficient r,
sometimes
called Pearson’s r. Given two time series, X and Y with means X’ and Y’,
each
with n elements, the formula for r is
S (xi –
X’) (yi – Y’)
(S (xi
– X’)2 S(yi
– Y’)2)1/2
29.
What is Auto regression?
Auto
regression is a method of predicting a future time series value by looking at
previous
values. Given a time series X = (x1,x2,….xn) a future value, x n+1, can be found
using
x
n+1 = x + j nx n
+ j n-1x n-1 +……+ e n+1
Here
e n+1 represents a random error, at time n+1.In addition, each element
in the time
series
can be viewed as a combination of a random error and a linear combination of
previous
values.
UNIT-IV
1.Define
data warehouse?
A
data warehouse is a repository of multiple heterogeneous data sources
organized
under a unified schema at a single site to facilitate management decision
making
.
(or)
A
data warehouse is a subject-oriented, time-variant and nonvolatile
collection
of data in support of management’s decision-making process.
2.What
are operational databases?
Organizations
maintain large database that are updated by daily transactions are
called
operational databases.
3.Define
OLTP?
If
an on-line operational database systems is used for efficient retrieval,
efficient
storage
and management of large amounts of data, then the system is said to be on-line
transaction
processing.
4.Define
OLAP?
Data
warehouse systems serves users (or) knowledge workers in the role of data
analysis
and decision-making. Such systems can organize and present data in various
formats.
These systems are known as on-line analytical processing systems.
5.How
a database design is represented in OLTP systems?
Entity-relation
model
6.
How a database design is represented in OLAP systems?
Star
schema
Snowflake
schema
Fact
constellation schema
7.Write
short notes on multidimensional data model?
Data
warehouses and OLTP tools are based on a multidimensional data model.
This
model is used for the design of corporate data warehouses and department data
marts.
This model contains a Star schema, Snowflake schema and Fact constellation
schemas.
The core of the multidimensional model is the data cube.
8.Define
data cube?
It
consists of a large set of facts (or) measures and a number of dimensions.
9.What
are facts?
Facts
are numerical measures. Facts can also be considered as quantities by which
we
can analyze the relationship between dimensions.
10.What
are dimensions?
Dimensions
are the entities (or) perspectives with respect to an organization for
keeping
records and are hierarchical in nature.
11.Define
dimension table?
A
dimension table is used for describing the dimension.
(e.g.)
A dimension table for item may contain the attributes item_ name, brand and
type.
12.Define
fact table?
Fact
table contains the name of facts (or) measures as well as keys to each of the
related
dimensional tables.
13.What
are lattice of cuboids?
In
data warehousing research literature, a cube can also be called as cuboids. For
different
(or) set of dimensions, we can construct a lattice of cuboids, each showing the
data
at different level. The lattice of cuboids is also referred to as data cube.
14.What
is apex cuboid?
The
0-D cuboid which holds the highest level of summarization is called the apex
cuboid.
The apex cuboid is typically denoted by all.
15.List
out the components of star schema?
_
A large central table (fact table) containing
the bulk of data with no
redundancy.
_
A set of smaller attendant tables (dimension
tables), one for each
dimension.
16.What
is snowflake schema?
The
snowflake schema is a variant of the star schema model, where some
dimension
tables are normalized thereby further splitting the tables in to additional
tables.
17.List
out the components of fact constellation schema?
This
requires multiple fact tables to share dimension tables. This kind of schema
can
be viewed as a collection of stars and hence it is known as galaxy schema (or)
fact
constellation
schema.
18.Point
out the major difference between the star schema and the snowflake
schema?
The
dimension table of the snowflake schema model may be kept in normalized
form
to reduce redundancies. Such a table is easy to maintain and saves storage
space.
19.Which
is popular in the data warehouse design, star schema model (or)
snowflake
schema model?
Star
schema model, because the snowflake structure can reduce the effectiveness
and
more joins will be needed to execute a query.
20.Define
concept hierarchy?
A
concept hierarchy defines a sequence of mappings from a set of low-level
concepts
to higher-level concepts.
21.Define
total order?
If
the attributes of a dimension which forms a concept hierarchy such as
“street<city<
province_or_state <country”, then it is said to be total order.
Country
Province
or state
City
Street
Fig:
Partial order for location
22.Define
partial order?
If
the attributes of a dimension which forms a lattice such as
“day<{month<quarter;
week}<year, then it is said to be partial order.
23.Define
schema hierarchy?
A
concept hierarchy that is a total (or) partial order among attributes in a
database
schema
is called a schema hierarchy.
24.List
out the OLAP operations in multidimensional data model?
_
Roll-up
_
Drill-down
_
Slice and dice
_
Pivot (or) rotate
25.What
is roll-up operation?
The
roll-up operation is also called drill-up operation which performs aggregation
on
a data cube either by climbing up a concept hierarchy for a dimension (or) by
dimension
reduction.
26.What
is drill-down operation?
Drill-down
is the reverse of roll-up operation. It navigates from less detailed data
to
more detailed data. Drill-down operation can be taken place by stepping down a
concept
hierarchy for a dimension.
27.What
is slice operation?
The
slice operation performs a selection on one dimension of the cube resulting in
a
sub cube.
28.What
is dice operation?
The
dice operation defines a sub cube by performing a selection on two (or) more
dimensions.
29.What
is pivot operation?
This
is a visualization operation that rotates the data axes in an alternative
presentation
of the data.
30.List
out the views in the design of a data warehouse?
_
Top-down view
_
Data source view
_
Data warehouse view
_
Business query view
31.What
are the methods for developing large software systems?
_
Waterfall method
_
Spiral method
32.How
the operation is performed in waterfall method?
The
waterfall method performs a structured and systematic analysis at each step
before
proceeding to the next, which is like a waterfall falling from one step to the
next.
33.How
the operation is performed in spiral method?
The
spiral method involves the rapid generation of increasingly functional
systems,
with short intervals between successive releases. This is considered as a good
choice
for the data warehouse development especially for data marts, because the turn
around
time is short, modifications can be done quickly and new designs and
technologies
can be adapted in a timely manner.
34.List
out the steps of the data warehouse design process?
_
Choose a business process to model.
_
Choose the grain of the business process
_
Choose the dimensions that will apply to each
fact table record.
_
Choose the measures that will populate each
fact table record.
35.Define
ROLAP?
The
ROLAP model is an extended relational DBMS that maps operations on
multidimensional
data to standard relational operations.
36.Define
MOLAP?
The
MOLAP model is a special purpose server that directly implements
multidimensional
data and operations.
37.Define
HOLAP?
The
hybrid OLAP approach combines ROLAP and MOLAP technology,
benefiting
from the greater scalability of ROLAP and the faster computation of
MOLAP,(i.e.)
a HOLAP server may allow large volumes of detail data to be stored in a
relational
database, while aggregations are kept in a separate MOLAP store.
38.What
is enterprise warehouse?
An
enterprise warehouse collects all the information’s about subjects spanning the
entire
organization. It provides corporate-wide data integration, usually from one
(or)
more
operational systems (or) external information providers. It contains detailed
data as
well
as summarized data and can range in size from a few giga bytes to hundreds of
giga
bytes,
tera bytes (or) beyond. An enterprise data warehouse may be implemented on
traditional
mainframes, UNIX super servers (or) parallel architecture platforms. It
requires
business modeling and may take years to design and build.
39.What
is data mart?
Data
mart is a database that contains a subset of data present in a data warehouse.
Data
marts are created to structure the data in a data warehouse according to issues
such
as
hardware platforms and access control strategies. We can divide a data
warehouse into
data
marts after the data warehouse has been created. Data marts are usually
implemented
on
low-cost departmental servers that are UNIX (or) windows/NT based. The
implementation
cycle of the data mart is likely to be measured in weeks rather than
months
(or) years.
40.What
are dependent and independent data marts?
Dependent
data marts are sourced directly from enterprise data warehouses.
Independent
data marts are data captured from one (or) more operational systems (or)
external
information providers (or) data generated locally with in particular department
(or)
geographic area.
41.What
is virtual warehouse?
A
virtual warehouse is a set of views over operational databases. For efficient
query
processing, only some of the possible summary views may be materialized. A
virtual
warehouse is easy to build but requires excess capability on operational
database
servers.
42.Define
indexing?
Indexing
is a technique, which is used for efficient data retrieval (or) accessing
data
in a faster manner. When a table grows in volume, the indexes also increase in
size
requiring
more storage.
43.What
are the types of indexing?
_
B-Tree indexing
_
Bit map indexing
_
Join indexing
44.Define
metadata?
Metadata
is used in data warehouse is used for describing data about data.
(i.e.)
meta data are the data that define warehouse objects. Metadata are created for
the
data
names and definitions of the given warehouse.
45.Define
VLDB?
Very
Large Data Base. If a database whose size is greater than 100GB, then
the
database is said to be very large database.
UNIT
– V
1.What
are the classifications of tools for data mining?
• Commercial Tools
• Public domain Tools
• Research prototypes
2.What
are commercial tools?
Commercial
tools can be defined as the following products and usually are
associated
with the consulting activity by the same company:
1.
‘Intelligent Miner’ from IBM
2.
‘SAS’ System from SAS Institute
3.
‘Thought’ from Right Information Systems. etc
3.
What are Public domain Tools?
Public
domain Tools are largely freeware with just registration fees:
’Brute’
from University of Washington. ‘MC++’ from Stanford university, Stanford,
California.
4.
What are Research prototypes?
Some
of the research products may find their way into commercial
market:
‘DB Miner’ from Simon Fraser University, British Columbia, ‘Mining Kernel
System’
from University of Ulster, North Ireland.
5.What
is the difference between generic single-task tools and generic multi-task
tools?
Generic
single-task tools generally use neural networks or decision trees.
They
cover only the data mining part and require extensive pre-processing and
postprocessing
steps.
Generic
multi-task tools offer modules for pre-processing and postprocessing
steps
and also offer a broad selection of several popular data mining
algorithms
as clustering.
6.
What are the areas in which data warehouses are used in present and in future?
The
potential subject areas in which data ware houses may be developed at
present
and also in future are
1.Census
data:
The
registrar general and census commissioner of India decennially
compiles
information of all individuals, villages, population groups, etc. This
information
is
wide ranging such as the individual slip. A compilation of information of
individual
households,
of which a database of 5%sample is maintained for analysis. A data
warehouse
can be built from this database upon which OLAP techniques can be applied,
Data
mining also can be performed for analysis and knowledge discovery
2.Prices
of Essential Commodities
The
ministry of food and civil supplies, Government of India complies
daily
data for about 300 observation centers in the entire country on the prices of
essential
commodities such as rice, edible oil etc, A data warehouse can be built
for
this data and OLAP techniques can be applied for its analysis
7.
What are the other areas for Data warehousing and data mining?
• Agriculture
• Rural development
• Health
• Planning
• Education
• Commerce and Trade
8.
Specify some of the sectors in which data warehousing and data mining are used?
• Tourism
• Program Implementation
• Revenue
• Economic Affairs
• Audit and Accounts
9.
Describe the use of DBMiner.
Used
to perform data mining functions, including characterization,
association,
classification, prediction and clustering.
10.
Applications of DBMiner.
The
DBMiner system can be used as a general-purpose online analytical
mining
system for both OLAP and data mining in relational database and
datawarehouses.
Used
in medium to large relational databases with fast response time.
11.
Give some data mining tools.
DBMiner
GeoMiner
Multimedia
miner
WeblogMiner
12.
Mention some of the application areas of data mining
DNA
analysis
Financial
data analysis
Retail
Industry
Telecommunication
industry
Market
analysis
Banking
industry
Health
care analysis.
13.
Differentiate data query and knowledge query
A
data query finds concrete data stored in a database and corresponds to a
basic
retrieval statement in a database system.
A
knowledge query finds rules, patterns and other kinds of knowledge in a
database
and corresponds to querying database knowledge including
deduction
rules, integrity constraints, generalized rules, frequent patterns and
other
regularities.
14.Differentiate
direct query answering and intelligent query answering.
Direct
query answering means that a query answers by returning exactly what
is
being asked.
Intelligent
query answering consists of analyzing the intent of query and
providing
generalized, neighborhood, or associated information relevant to the
query.
15.
Define visual data mining
Discovers
implicit and useful knowledge from large data sets using data and/
or
knowledge visualization techniques.
Integration
of data visualization and data mining.
16.
What does audio data mining mean?
Uses
audio signals to indicate patterns of data or the features of data mining
results.
Patterns
are transformed into sound and music.
To
identify interesting or unusual patterns by listening pitches, rhythms, tune
and
melody.
Steps
involved in DNA analysis
Semantic
integration of heterogeneous, distributed genome databases
Similarity
search and comparison among DNA sequences
Association
analysis: Identification of co-occuring gene sequences
Path
analysis: Linking genes to different stages of disease development
Visualization
tools and genetic data analysis
17.What
are the factors involved while choosing data mining system?
Data
types
System
issues
Data
sources
Data
Mining functions and methodologies
Coupling
data mining with database and/or data warehouse systems
Scalability
Visualization
tools
Data
mining query language and graphical user interface.
18.
Define DMQL
Data
Mining Query Language
It
specifies clauses and syntaxes for performing different types of data mining
tasks
for example data classification, data clustering and mining association
rules.
Also it uses SQl-like syntaxes to mine databases.
19.
Define text mining
Extraction
of meaningful information from large amounts free format textual
data.
Useful
in Artificial intelligence and pattern matching
Also
known as text mining, knowledge discovery from text, or content
analysis.
20.
What does web mining mean
Technique
to process information available on web and search for useful data.
To
discover web pages, text documents , multimedia files, images, and other
types
of resources from web.
Used
in several fields such as E-commerce, information filtering, fraud
detection
and education and research.
21.Define
spatial data mining.
Extracting
undiscovered and implied spatial information.
Spatial
data: Data that is associated with a location
Used
in several fields such as geography, geology, medical imaging etc.
22.
Explain multimedia data mining.
Mines
large data bases.
Does
not retrieve any specific information from multimedia databases
Derive
new relationships , trends, and patterns from stored multimedia data
mining.
Used
in medical diagnosis, stock markets ,Animation industry, Airline
industry,
Traffic management systems, Surveillance systems etc.
16
MARKS QUESTIONS AND ANSWERS
UNIT-I
1.
Explain the evolution of Database technology?
_
Data collection and Database creation
_
Database management systems
_
Advanced database systems
_
Data warehousing and Data Mining
_
Web-based Database systems
_
New generation of Integrated information
systems
2.Explain the steps of
knowledge discovery in databases?
_
Data cleaning
_
Data integration
_
Data selection
_
Data transformation
_
Data mining
_
Pattern evaluation
_
Knowledge presentation
3.
Explain the architecture of data mining system?
_
Database, datawarehouse, or other information
repository
_
Database or data warehouse server
_
Knowledge base
_
Data mining engine
_
Pattern evaluation module
_
Graphical user interface
4.Explain
various tasks in data mining?
(Or)
Explain
the taxonomy of data mining tasks?
_
Predictive modeling
• Classification
• Regression
• Time series analysis
_
Descriptive modeling
• Clustering
• Summarization
• Association rules
• Sequence discovery
5.Explain
various techniques in data mining?
_
Statistics (or) Statistical perspectives
_
Point estimation
• Data summarization
• Bayesian techniques
• Hypothesis testing
• Correlation
_
Regression
_
Machine learning
_
Decision trees
_
Hidden markov models
_
Artificial neural networks
_
Genetic algorithms
_
Meta learning
UNIT-II
6.Explain
the issues regarding classification and prediction?
_
Preparing the data for classification and
prediction
o Data cleaning
o Relevance analysis
o Data transformation
_
Comparing classification methods
o Predictive accuracy
o Speed
o Robustness
o Scalability
o Interpretability
7.Explain
classification by Decision tree induction?
_
Decision tree induction
_
Attribute selection measure.
_
Tree pruning
_
Extracting classification rules from decision
trees
8.Write
short notes on patterns?
_
Pattern definition
_
Objective measures
_
Subjective measures
_
Can a data mining system generate all of the
interesting
patterns?
_
Can a data mining system generate only
interesting
patterns?
9.Explain
mining single –dimensional Boolean associated rules from transactional
databases?
_
The apriori algorithm: Finding frequent
itemsets using
candidate
generation
_
Mining frequent item sets without candidate
generation
10.Explain
apriori algorithm?
_
Apriori property
_
Join steps
_
Prune step
_
Example
_
Algorithm
11.Explain
how the efficiency of apriori is improved?
_
Hash-based technique (hashing item set
counts)
_
Transaction reduction (reducing the number of
transactions
scanned
in future iteration)
_
Partitioning (Partitioning the data to find
candidate item sets)
_
Sampling (mining on a subset of the given
data)
_
Dynamic item set counting (adding candidate
item sets at
different
points during a scan)
12.Explain
frequent item set without candidate without candidate generation?
_
Frequent patterns growth (or) FP-growth
_
Frequent pattern tree (or) FP-tree
_
Algorithm
13.
Explain mining Multi-dimensional Boolean association rules from transaction
databases?
_
Multi-dimensional (or) Multilevel association
rules
_
Approaches to mining Multilevel association
rules
• Using uniform minimum support for all levels
• Using reduced minimum support at lower levels
o Level-by-level independent
o Level-cross filtering by single
o Level- cross filtering by k-item set
_
Checking for redundant Multilevel association
rules
14.Explain
constraint-based association mining?
_
Knowledge type constraints
_
Data constraints
_
Dimension/level constraints
_
Interestingness constraints
_
Rule constraints
_
Metarule-Guided mining of association of
association
rules
_
Mining guided by additional rule constraints
Unit
–III
15.Explain
regression in predictive modeling?
_
Regression definition
_
Linear regression
_
Multiple regression
_
Non-linear regression
_
Other regression models
16.Explain
statistical perspective in data mining?
_
Point estimation
_
Data summarization
_
Bayesian techniques
_
Hypothesis testing
_
Regression
_
Correlation
17.
Explain Bayesian classification.
_
Bayesian theorem
_
Naïve Bayesian classification
_
Bayesian belief networks
_
Bayesian learning
18.
Discuss the requirements of clustering in data mining.
_
Scalability
_
Ability to deal with different types of
attributes
_
Discovery of clusters with arbitrary shape
_
Minimal requirements for domain knowledge to
determine
input
parameters
_
Ability to deal with noisy data
_
Insensitivity to the order of input records
_
High dimensionality
_
Interpretability and usability
_
Interval scaled variables
_
Binary variables
o Symmetric binary variables
o Asymmetric binary variables
_
Nominal variables
_
Ordinal variables
_
Ratio-scaled variables
20.
Explain the partitioning method of clustering.
K-means
clustering
K-medoids
clustering
21.
Explain Visualization in data mining.
Various
forms of visualizing the discovered patterns
_
Rules
_
Table
_
Crosstab
_
Pie chart
_
Bar chart
_
Decision tree
_
Data cube
_
Histogram
_
Quantile plots
_
q-q plots
_
Scatter plots
_
Loess curves
UNIT
IV
22.
Discuss the components of data warehouse.
_
Subject-oriented
_
Integrated
_
Time-Variant
_
Non-volatile
23.
List out the differences between OLTP and OLAP.
_
Users and system orientation
_
Data contents
_
Database design
_
View
_
Access patterns
24.Discuss
the various schematic representations in multidimensional model.
_
Star schema
_
Snow flake schema
_
Fact constellation schema
25.
Explain the OLAP operations I multidimensional model.
_
Roll-up
_
Drill-down
_
Slice and dice
_
Pivot or rotate
26.
Explain the design and construction of a data warehouse.
_
Design of a data warehouse
• Top-down view
• Data source view
• Data warehouse view
• Business query view
_
Process of data warehouse design
27.Expalin
the three-tier data warehouse architecture.
_
Warehouse database server(Bottom tier)
_
OLAP server(middle tier)
_
Client(top tier)
28.
Explain indexing.
_
Definition
_
B-Tree indexing
_
Bit-map indexing
_
Join indexing
29.Write
notes on metadata repository.
_
Definition
_
Structure of the data warehouse
_
Operational metadata
_
Algorithms used for summarization
_
Mapping from operational environment to data
warehouse
_
Data related to system performance
_
Business metadata
30.
Write short notes on VLDB.
_
Definition
_
Challenge related to database technologies
_
Issues in VLDB
UNIT
V
31.Explain
data mining applications for Biomedical and DNA data analysis.
_
Semantic integration of heterogeneous,
distributed genome databases
_
Similarity search and comparison among DNA
sequences
_
Association analysis.
_
Path analysis
_
Visualization tools and genetic data
analysis.
32.
Explain data mining applications fro financial data analysis.
_
Loan payment prediction and customer credit
policy analysis.
_
Classification and clustering of customers
fro targeted marketing.
_
Detection of money laundering and other
financial crimes.
33.
Explain data mining applications for retail industry.
_
Multidimensional analysis of sales,
customers, products, time and region.
_
Analysis of the effectiveness of sales
campaigns.
_
Customer retention-analysis of customer
loyalty.
_
Purchase recommendation and cross-reference
of items.
34.
Explain data mining applications for Telecommunication industry.
_
Multidimensional analysis of
telecommunication data.
_
Fraudulent pattern analysis and the
identification of unusual patterns.
_
Multidimensional association and sequential
pattern analysis
_
Use of visualization tools in
telecommunication data analysis.
35.
Explain DBMiner tool in data mining.
_
System architecture
_
Input and Output
_
Data mining tasks supported by the system
_
Support of task and method selection
_
Support of the KDD process
_
Main applications
_
Current status
36.
Explain how data mining is used in health care analysis.
_
Health care data mining and its aims
_
Health care data mining technique
_
Segmenting patients into groups
_
Identifying patients into groups
_
Identifying patients with recurring health
problems
_
Relation between disease and symptoms
_
Curbing the treatment costs
_
Predicting medical diagnosis
_
Medical research
_
Hospital administration
_
Applications of data mining in health care
_
Conclusion
37.
Explain how data mining is used in banking industry.
_
Data collected by data mining in banking
_
Banking data mining tools
_
Mining customer data of bank
_
Mining for prediction and forecasting
_
Mining for fraud detection
_
Mining for cross selling bank services
_
Mining for identifying customer preferences
_
Applications of data mining in banking
_
Conclusion
38.
Explain the types of data mining.
_
Audio data mining
_
Video data mining
_
Image data mining
_
Scientific and statistical data mining
