[Mid 1 - DMDW]
car => financial from bank [loan=80 %, insurance=20 %] _ _ _ _ _ _ _ & _ _ _ _ _ are two
measures of Association rules.
a. car,bank
b. bank.loan
c. loan,insurance
d. bank,loan,insurance
The set X=>Y [a=50 %,b=2 %] is a _ _ _ _ _ _ _ _ _ _ _ itemset.
a. 1
b. 2
c. 3
d. 4
6.
7.

percent(A,"70,71 _ _ _ 80") => placement(A,"Infosys")
percent(A,"70,71 _ _ _ 80") => placement(A, "Microsoft")
percent(A,"70,71 _ _ _ _ 80") => placement(A," Dell")
percent(A,"70,71 _ _ _ _ 80") => placement(A,"IBM")
These set of rules clearly refer to _ _ _ _ _ _ _ _ _ _ _ _ _ _ rule
a. Boolean association
b. Quantitative association
c. Single dimensional association
d. Multi dimensional association

The following rule Age(A,"20,21 _ _ _ _ 27") Λ percent(A,"60,61 _ _ _ 80") Λ test(A,"B,B+ _ _ _
_ .A+) =>placement(A,"MNCs") is an example of _ _ _ _ _ _ _ _ rule.
a. Boolean association
b. Quantitative association
c. Single dimensional association
d. Multi dimensional association
A set of items is referred to as a(n) _ _ _ _ _ _ _ _ _ _
a. itemset
b. item set
c. set entity
d. entity set
1.
2.
3        .
car =>financial from bank [loan=80 %,insuranace=20 %] Association rules are satisfied if they
have _ _ _ _ _ _ _ _
a. maximum carshops,maximum bank branches
b. maximum banks, maximum loan threshold
c. maximum loan threshold, minimum insurance threshold
d. maximum bank threshold, maximum loan threshold, minimum insurance threshold
4.
If X=>Y[a=50 %,b=2 %] a(X=>Y) = _ _ _ _ _ _ _ _ _ _ & _ _ _ _ _ _ _ _ and b(X=>Y)= _ _ _ _ _
_____
a. P(AUB),P(A/B)
b. P(AUB),P(B/A)
c. P(A∩B),P(A/B)
d. P(A∩B),P(B/A)
5.
TID stands for _ _ _ _ _ _ _ _ _ _ _
a. Transaction is associated with an identifier
b. Transaction is Differentiable
c. Transaction identifier
d. Transaction is dissociated with an identifier
9.
If a rule concerns associations between the presence or absence of items, it is a _ _ _ _ _ _
_ _ _ _ _ _ rule.
a. Boolean association
b. Quantitative association
c. Frequent association
d. Transaction association
8.

10.
If the transactional data is
The minimum support count is _ _ _ _ _ _ _ _ _
a.
b.
c.
d.
1
2
3
4
11						.
Apriori algorithm employs level-wise search, where k-itemsets uses ------ itemsets.
a. k
b. (k-1)
c. (k+1)
d.
12.
Which step could involve huge computations?
a. join step
b. prune step
c. calculation step
d. logical step

13.
Anti-monotone states _ _ _ _ _ _ _ _
a. if a set cannot pass a test, all its supersets also cannot pass the same test
b. if a set cannot pass a test, all its supersets pass the test
c. if a set pass a test, all its supersets cannot pass the same test
d. Is a set pass a test, all its subsets cannot pass the same test.
Transaction reduction implies _ _ _ _ _ _
a. reducing the number of transactions scanned till previous iterations
b. reducing the number of transactions in the current iteration
c. reducing the number of transactions in the future iteration
d. reducing the number of iterations in a transaction
_ _ _ _ _ _ _ _ _ is used to improve the efficient of the Apriori algorithm.
a. Berg queries
b. Iceberg queries
c. Ice Burg queries
d. Ice Cube queries
Which threshold can be set up for passing down relatively frequent items to lower levels?
a. level-class threshold
b. level-shift threshold
c. level-passage threshold
d. level-jump threshold
When multi-level association rules are mined, some of the rules found will be redundant due to _
_ _ _ _ _ _ _ _ _ _ relationships between them.
a. hierarchical
b. multi-level
c. single-dimension
d. ancestor
14.
15.
16.
17.
18.
Consider the following rile
Age(A,"18,19, _ _ _ _ _ 29") Λ placement(A,"Infosys,IBM, _ _ _ _ ") Λ purchases(A,"mobile") =>
purchases(A,"high memory card") Λ purchases(A,"card reader")
This rule is highlighted in saying that it has _ _ _ _ _ _ _ _
a. multiple predicated
b. single predicate
c. repetitive predicate
d. dependent predicate
The correlation between the occurrence of A and B can be measured by computing _ _ _ _ _ _ _ _
______
a. corrA,B =P(A) / P(B)
b. corrA,B=(P(A)P(B)) / P(A∩B)
c. corrA,B = P(A∪B) / (P(A)P(B))
d. corrA,B = P(A∩B) / (P(A)P(B))
19.
20.
Which association rule has overcome the disadvantage of Association rules?
a. quantitative association rules
b. Distance-based association rules
c. single dimensional association rules
d. Multi dimensional association rules
Let A[X] be the set of 'n' tuples t1,t2, _ _ _ _ _ _ _ _ tn projected on the attribute set X . Which
measure of A[X] is the average pair wise distance between the tuples projected on X?
a. radius
b. diameter
c. density
d. frequency
If a single distinct predicate exists in single dimensional association rule , it is also called as _ _ _
_______
a. intra dimension association rule
b. inter dimension association rule
c. extra dimension association rule
d. quantitative association rule
If no repeated predicates exists in multi dimensional association rule is also called as _ _ _ _ 
a.
  intra dimension association rule
b. inter dimension association rule
c. extra dimension association rule
d. quantitative association rule
If in multi dimensional association rule with repeated predicates, which contains multiple
occurrences of some predicate certain rules are called as _ _ _ _ _ _ _ _ _
a. repeatitive association rule
b. recursive association rule
c. mixed association rule
d. hybrid association rule
The partitioning of process is referred to as binning and the intervals are considered as _ _ _ _
a. bins
b. modules
c. segments
d. time laps
21.
22.
23.
24.
25.

26.
Which algorithm is included with certain series of walks through itemset space?
a. Apriori algorithm
b. Ancestor algorithm
c. random walk through algorithm
d. sequential walk through algorithm
Which categories can be used during association mining to guide the process, leading to more
efficient and effective mining?
a. antimonotone, monotone, succinct, convertible
b. antimonotone, monotone, succinct, inconvertible
c. antimonotone, monotone, convertible, inconvertible
d. antimonotone, succinct, convertible, iconvertible
27.
28.
29.
A constraint such as "avg(I.marks) <= 70" is not a(n) _ _ _ _ _ _ _ _ _ _ _
a. anti-monotone
b. monotone
c. succinct
d. convertible
A constraint "max(I.marks) >=600" is acceptable for _ _ _ _ _ _ & _ _ _ _ _ _ _ categories.
a. antimonotone,monotone
b. monotone,succinct
c. antimonotone,succinct
d. succinct,convertible
The constraint "max(I.marks)<=600" is acceptable by _ _ _ _ _ _ _ _ & _ _ _ _ _ _ _ _ categories.
a. antimonotone,monotone
b. monotone,succinct
c. antimonotone,succinct
d. succinct,convertible
Which constraint specify the set of task-relevant data?
a. knowledge type constraints
b. data constraints
c. task-oriented constraints
d. rule constraints
Which constraints may be expressed as Metarules?
a. knowledge type constraints
b. data constraints
c. interestingness constraints
d. rule constraints
Anti-monotone, monotone, succinct, convertible and inconvertible are five different categories of
_ _ _ _ _ _ _ _ _ _ constraints.
a. knowledge type constraints
b. data constraints
c. interestingness constraints
d. rule constraints
Which constraints are applied before mining?
a. knowledge type and data constraints
b. data and dimension constraints
c. dimension and rule
d. knowledge and rule constraints
30.
31.
32.
33.
34.

35.
The constraint "support(s)
a.
b.
c.
d.
Antimonotone
monotone
succinct
Anti-succinct
is acceptable by _ _ _ _ _ _ _ _ _ _ category.
36.
Preprocessing of data in preparation for classification and prediction can involve ------ for
normalizing the data.
a. data cleaning
b. relevance analysis
c. data transformation
d. data redundancy
While _ _ _ _ _ _ _ predicts class, _ _ _ _ _ _ _ models continuous-valued functions.
a. prediction-classification
b. classification-prediction
c. speed-scalability
d. scalability-speed
_ _ _ _ _ _ _ _ & _ _ _ _ _ _ _ are the two major types of prediction problems.
a. classification-regression
b. classification-data cleaning
c. data cleaning-predictive accuracy
d. regressive-scalability
Which of the following criteria is not the one used for the comparison of classification and
prediction?
a. speed
b. predictive accuracy
c. data cleaning
d. interpretability
_ _ _ _ _ _ _ _ _ _ _ is a two step process
a. data classification
b. data prediction
c. data hiding
d. data abstraction
The data tuples analyzed to build the model collectively form _ _ _ _ _ _ _ _ _ 
a. samples
b. training data set
c. untrained data set
d. supervised data set
In _ _ _ _ _ _ _ _ _ the class label of each training sample is not known, and the number or set of
classes to be learned may not be known in advance.
a. supervised learning
b. unsupervised learning
c. authorized learning
d. unauthorized learning
_ _ _ _ _ _ _ is a simple technique that uses a test set of class-labeled samples.
a. clustering method
b. holdout method
c. data classification
d. data learning
37.
38.
39.
40.
41.
42.
43.

45.
Early decision tree algorithms typically assume that the data is from _ _ _ _
a. memory
b. user input from keyboard
c. dynamic user input
d. mouse click
Decision trees can easily be converted to _ _ _ _ _ _ _ rules.
a. IF
b. Nested IF
c. If-THEN
d. GROUP BY
During the construction of decision tree induction the tree starts as _ _ _ _ _ _ _ _ _
a. single node
b. dual child node
c. binary tree nodes
d. multi-valued nodes
46.
47.
48.
_ _ _ _ _ _ _ _ _ _ uses the concept to generalize the data by replacing lower-level data with high-
level concepts.
a. analysis oriented induction
b. algorithm oriented induction
c. attribute oriented induction
d. approach oriented induction
In
a.
b.
c.
d.
a decision tree, _ _ _ _ _ _ _ _ represents an outcome of the test.
internal node
branch
leaf nodes
root
49.
50.
The basic algorithm for decision tree induction is _ _ _ _ _ _ _ _
a. knapsack algorithm
b. greedy algorithm
c. traveling sales person algorithm
d. 0/1 knapsack algorithm
_ _ _ _ _ _ _ _ _ _ _ methods use statistical measures to remove the least reliable branches.
a. tree pruning
b. fragmentation
c. segmentation
d. classification
In
a.
b.
c.
d.
how many approaches does tree pruning work?
1
2
3
4
51.
52.
_ _ _ _ _ _ _ _ _ refers to the preprocessing of data in order to remove noise.
a. predictive accuracy
b. robustness
c. data cleaning
d. Interpretability
44.

iii)descendent
iv)immediate successor
a. only i
b. i & ii
c. only iii
d. iii & iv
55.
Bayes theorem provides a way of calculating which probability?
a. posterior
b. prior
c. stable
d. ideal
In Gaussian density function
a.
b.
c.
d.
Standard deviation(SD) & mean
mean & SD
mode & SD
SD & median
stands for _ _ _ _ _ _ & _ _ _ _ _ _ _ _ _ _
56.
57.
P(H/X) is a _ _ _ _ _ _ _ _ probability.
a. posterior
b. prior
c. stable
d. ideal
Nave Bayesian classifier is also called as _ _ _ _ _ _ _ _ _ _ classifier.
a. computational Bayesian
b. simple Bayesian
c. non-computational Bayesian
d. Complexed Bayesian
CPT stands for _ _ _ _ _ _ _ _ _ _ _
a. Computer preparation test
b. Computational probability test
c. Conditional probability table
d. Computational probability table
During Bayesian n/w s incomplete data is referred to _ _ _ _ _ _ _ _ _ _
a. input data
b. Hidden data
c. output data
d. recursive data
58.
59.
60.
If an arc is drawn from node A to node B then A is _ _ _ _ _ _ _ _ _ of B
i)Parent
ii)immediate predecessor
54.
Classification threshold is also called as _ _ _ _ _ _ _ _ _ _ _
a. exception threshold
b. SLIQ threshold
c. precision threshold
d. threading threshold
53.

61.
A Neural Network containing N hidden Layers is called as _ _ _ _ _ _ _ _ _ _ Neural network
layered
a. (N-1)
b. N
c. (N+1)
d. 2N
_ _ _ _ _ _ _ _ _ _ _ are modified so as to minimize the mean squared error b/w the networks
prediction and the actual class
a. no of hidden layers
b. weights of the nodes
c. no of nodes
d. no of inputs to a node
Back propagation is a neural n/w _ _ _ _ _ _ _ _ _ _ _ algorithm
a. updation
b. learning
c. comparison
d. data mining
Neural n/w learning is also referred to as _ _ _ _ _ _ _ _ _ learning
a. sequential
b. connectionist
c. dependent
d. random
In a multilayer feed-forward NN the weighted output of hidden layer are inputs to _ _ _ _ _ _ _ 
a. input layer
b. next hidden layer
c. output layer
d. input to another NN
62.
63.
64.
65.
66.
Erri =
a.
b.
c.
d.
where
are _ _ _ _ _ _ _ _ _ & _ _ _ _ _ _ _ _ _
desired o/p, true o/p
actual o/p, true o/p
desired o/p, error o/p
actual o/p, error o/p
67.
The tech of updating the weights & biases after the presentation of each sample is refered to As _
a. node updating
b. case updating
c. epoch updating
d. layer updating
If in the item set {percent <= "59", placement ="no" } whose support increases from 0.7 % is c1
to 92.6 % in c2, the growth rate is _ _ _ _ _ _ _ _ _
a. 0.7 %/92.6 %
b. 92.6 %/0.7 %
68.

c.
d.
69.
(92.6 %-0.7 %)/0.7 %
92.6 %/(92.6 %-0.7 %)
The statement "If any student age is above 20 and their percentage is 70 or more are approved
for placement " the rule in rough set theory can be written as _ _ _ _ _ _ _ _ _ _ _ _
a. if(x, age > 20)Λ if(x, percentage >= 70) then placement ="approved"
b. if(x, age > 20) Λ if(percentage >= 70) then placement ="approved"
c. if(age > 20) Λ if(percentage >= 70) ?> placement ="approved"
d. if(x, age > 20) Λ if(x, percentage >= 70) then placement = approved
_ _ _ _ _ _ _ _ _ _ _ is defined in terms of Euclidean distance
a. Min distance between two points
b. max distance between 2 points
c. Closeness between 2 points
d. mean between 2 points
The rule "IF NOT A1 AND A2 THEN NOT C1 "is encoded as
a. 101
b. 010
c. 001
d. 110
If a set of rules has same condent,then the rule with highest confidence is selected as _ _ _ 
_ _ _ _ to represent the set
a. probability rule
b. possible rule
c. posterior rule
d. proportionality rule
EP
a.
b.
c.
d.
stands for _ _ _ _ _ _ _ _ _ _ _
emerging point
evolving point
emerging patterns
evolving patters
70.
71.
72.
73.
74.
JEP is a special case of EP , where J stands for _ _ _ _ _ _ _ _ _ _ _ _ _ 
a. joint
b. jazzing
c. jagging
d. jumping
_ _ _ _ _ _ _ _ _ are used to incorporate ideas if natural evolution
a. case based reasoning
b. genetic algorithms
c. rough set theory
d. fuzzy set approach
Rough set theory is based on _ _ _ _ _ _ _ _ _ _ classes with in fn training date
a. symmetric
b. transitive
c. equivalence
d. trichotonomy
In
a.
b.
c.
d.
which operation Substring from pairs of rules are swapped to form new pair of rules
equivalence
CBR
crossover
mutation
75.
76.
77.

79.
Assume the fallow data
X(years experience ) y( salary)
02 ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; 13000
09 ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; 45000
15 ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; 75000
03 ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; 18000
Its β value is _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
a. 4698.6
b. 3698.6
c. 2698.6
d. 5698.6
80.
From the eg. Y=
a.
b.
c.
d.
Y-βX
Y-βx_
y'-βx
y'-
X, the co-efficient α= _ _ _ _ _ _ _ _ _ _ _ _
81.
Apart from prediction, the log linier model is also useful for _ _ _ _ _ _ _ _ _ _ _
a. image patterning
b. data compression
c. voice recognition
d. speech recognition
In Y=
a.
b.
c.
d.
X,
are _ _ _ _ _ _ _ _ _ _
82.
constants
regression coefficients
variable coefficients
averages of X and Y
83.
_ _ _ _ _ _ _ _ can be modeled by adding polynomial terms to the basic linear mode.
a. linear regression
b. multiple regression
c. polynomial regression
d. poison regression
_ _ _ _ _ _ _ _ regression can be modeled by adding polynomial terms to the basic linear model
a. Linear
b. multiple
c. polynomial
d. binomial
Which regression helps in counting the data frequently?
a. Linear regression
b. logistic regression
c. Poisson regression
d. multiple regression
84.
85.
Assume the following salary details: X(years experience) Y(salary in Rs.)
2 ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; 13000
9 ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; 45000
15 ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; 75000
4 ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; 18000
The value of x = _ _ _ _ _ _ _ _ _
a. 30
b. 8.0
c. 8.5
d. 9.0
78.

a.
b.
c.
d.
87.
holdout method
classifier
sensitivity
k-fold cross-validation
Sensitivity & specificity can be used to measure _ _ _ _ _ _ _ _ _ & _ _ _ _ _ _ _ _ 
a. +ve samples & -ve samples
b. -ve samples & +ve samples
c. speed & robustness
d. classification & prediction
In _ _ _ _ _ _ _ _ the class distribution of the samples in each fold is approximately the same as
that in the initial data.
a. random subsampling
b. k-fold cross validation
c. stratified cross-validation
d. boot strapping
Accuracy estimates also help in _ _ _ _ _ _ _ _ _ _ _ _
a. known behavior of future classifiers
b. comparison of different classifiers
c. selection of classifier
d. increasing classifier accuracy
Into how many independent sets the given data are randomly partitioned in holdout method?
a. 2
b. 3
c. 4
d. 5
Which method of estimating classifier samples the given the training instances uniformly with
replacement?
a. k-fold cross-validation
b. stratified cross validation
c. boot strapping
d. leave-one-out
_ _ _ _ _ _ _ & _ _ _ _ _ _ _ _are the techniques for approving overall classifier accuracy by
learning and combining series of individual classifiers.
a. bogging & boosting
b. bagging & banging
c. bagging & boosting
d. bagging &bogging
_ _ _ _ _ _ _ _ _ is used to access the percentage of samples.
a. sensitivity
b. specificity
c. precision
d. decision
Bootstrap is also known as _ _ _ _ _ _ _ _ _
a. bagging
b. bogging
c. boosting
d. banging
is _ _ _ _ _ _ _ _ _ _ _
a.
b.
c.
d.
simple match coefficient
Jaccard coefficient
transition coefficient
regression coefficient
88.
89.
90.
91.
92.
93.
94.
95.

Training set & test set are two sets of _ _ _ _ _ _ _ _ _ _ _
86.

d.
97.
number of matches and number of clusters
calculates _ _ _ _ _ _ _ _ _ _
Sp =
a.
b.
c.
d.
Euclidean distance
Manhattan distance
z-score
mean absolute deviation
98.
A _ _ _ _ _ _ _ _ _ resembles a nominal variable.
a. normal variable
b. discrete ordinal variable
c. continuous ordinal variable
d. poission ordinal variable
The process of grouping a set of physical objects into classes of similar objects is called as -
a. moduling
b. segmenting
c. clustering
d. machine learning
Clustering is a form of _ _ _ _ _ _ _ _ _ _ _
a. learning by practice
b. learning by examples
c. learning by observation
d. learning by testing
Interval-scaled variables are _ _ _ _ _ _ _ _ measurements of a linear scale.
a. discrete
b. continuous
c. differentiable
d. non-continuous
The data matrix is often called as _ _ _ _ _ _ _
a. one-mode matrix
b. two-mode matrix
c. zero-mode matrix
d. poly-node matrix
Object-by-object structure is also known as _ _ _ _ _ _ _ _ _ _ _ _
a. difference matrix
b. data matrix
c. Dissimilarity matrix
d. Identity matrix
99.
100.
101.
102.
103.
104.
the computational complexity of CLARANS is _ _ _ _ _ _ _ _ _ _
a. O(n)
b. O(n2)
c. O(log n)
d. O(n log n)
www.pvpsiddhartha.ac.in
number of mappings and number of patterns
number of matches and number of variables
number of mappings and number of clusters
a.
b.
c.
where m and p are _ _ _ _ _ _ _ _ _ _ _ _
96.

105.
_ _ _ _ _ _ _ _ _ can be used to find the most "natural" number of clusters using a silhouette
coefficient.
a. PAM
b. CLAPP
c. CLARA
d. CLARANS
The _ _ _ _ _ _ algorithm where each cluster is represented byy one of the objects located near
the center of cluster.
a. k-means
b. k-medians
c. k-medoids
d. k-modes
The agglomerative approach is also called as _ _ _ _ _ approach.
a. top-down
b. bottom-up
c. sequential
d. random
Most of the partitioning methods cluster objects are based on _ _ _ _ _ _ _ _
a. number of clusters
b. distance between objects
c. number of objects in each class
d. learning rate
_ _ _ _ _ _ _ _ _ _ methods quantize the object space into a finite number of cells that form a grid
structure.
a. hierarchical methods
b. density-based methods
c. grid-based methods
d. model-based methods
_ _ _ _ _ _ _ is a density-based method that computers an augmented clustering.
a. DBSCAN
b. OPTICS
c. STING
d. CLIQUE
EM is expanded to _ _ _ _ _ _ _ _ _
a. entity maximization
b. exception maximization
c. expectation maximization
d. earning maximization
Clustering large applications can be shortened as _ _ _ _ _ _ _ _ _
a. CLA
b. CLAPP
c. CLARA
d. CLULA
106.
107.
108.
109.
110.
111.
112.
113.
The absolute closeness between 2 clusters, normalized w.r.t the internal closeness of two clusters
is _ _ _ _ _ _ _ _ _ _
a. relative distance
b. relative interconnectivity
c. relative density

relative closeness
d.

114.
-------- is a triplet summary information about sub clusters of objects
a. clustering features
b. clustering feature tree
c. Chameleon
d. density feature
Which method overcame with the problem of favoring clusters with spherical shape and similar
sizes?
a. BIRCH
b. CURE
c. ROCK
d. STING
For given n objects the complexity of CURE is _ _ _ _ _ _ _ _ _ _
a. O(n)
b. O(n2)
c. O(log n)
d. O(n log n)
AGNES is expanded to _ _ _ _ _ _ _ _
a. agglomerative nesting
b. aglomative nesting
c. agnomative nesting
d. aggomerative nesting
Which method represents each cluster by a certain fixed number of representative objects and
shrinks them towards the center of the cluster
a. BIRCH
b. CURE
c. ROCK
d. DBSCAN
CF
a.
b.
c.
d.
tree has how many parameters?
5
4
3
2
115.
116.
117.
118.
119.
120.
Which algorithm has overcome with the weakness of CURE & ROCK algorithms?
a. DBSCAN
b. OPTICS
c. Chameleon
d. Wave Cluster
Which method doesn't handle categorical attributes?
a. BIRCH
b. CURE
c. ROCK
d. CLIQUE
Square wave influence function is set to 0(zero) if _ _ _ _ _ _ _ _ _
a. d(x,y)= σ
b. d(x,y)> σ
c. d(x,y)< σ
d. d(x,y ) ≠ σ
121.
122.

123.
Grid-based computation is _ _ _ _ _ _ _ _ _ _ _
a. cluster-independent
b. data independent
c. query independent
d. density independent
Which algorithm facilitates parallel processing?
a. STING
b. CLIQUE
c. OPTICS
d. ROCK
If a k-dimensional unit is dense , then its projections are in _ _ _ _ _ _ _ _ _ _ dimensional space.
a. k
b. (k-1)
c.(k+1)
d.
124.
125.
126.
DBSCAN is _ _ _ _ _ _ _ _ _ clustering algorithm
a. partitioning methods
b. hierarchical methods
c. density-based methods
d. grid-based methods
Density connectivity is a _ _ _ _ _ _ _ _ relation.
a. closure
b. symmetric
c. transitive
d. trichotonomy
The _ _ _ _ _ _ _ _ _ _ is an object is defined as the sum of influence functions of all data points
a. distance function
b. interconnectivity function
c. density function
d. closeness function
STING is _ _ _ _ _ _ _ _ clustering algorithm
a. partitioning methods
b. hierarchical methods
c. density-based methods
d. grid-based methods
Wave Cluster is _ _ _ _ _ _ _ _ algorithm from the following.
i)hierarchical
ii)density-based
iii)grid-based
a. i & ii
b. i & iii
c. ii & iii
d. only ii
_ _ _ _ _ _ _ can automatically result in the removal of outliers.
a. Wavelet transform
b. Wavelet outlier
c. data cube
d. DENCLUE
CLASSIT and Auto class are _ _ _ _ _ _ _ _ _ _
a. statistical approaches
b. neural network approaches
c. Wavelet transform
d. Outlier analysis
127.
128.
129.
130.
131.
132.

d.
134.
cluster learning
_ _ _ _ _ _ _ _ _ and _ _ _ _ _ _ _ _ _ are two operators of COBWEB.
a. merging, dividing
b. scratching, splitting
c. merging, splitting
d. adding, dividing
The larger the value, the greater the proportion of class members that share attribute-value pair
This is applicable t o _ _ _ _ _ _ _ _ _ _ _ _ _
a. interclass similarity
b. interclass dissimilarity
c. intraclass similarity
d. intraclass dissimilarity
The sequence of steps in conceptual clustering are _ _ _ _ _ _ _ _ _ _
a. characterization,clustering
b. classification,prediction
c. clustering,characterization
d. prediction,classification
_ _ _ _ _ _ _ has the ability to automatically adjust the number of classes in a partition.
a. CLIQUE
b. Wave cluster
c. COBWEB
d. Outlier analysis
SOM stands for _ _ _ _ _ _ _ _ _ _
a. self organizing maps
b. self originating maps
c. self outlier maps
d. self online maps
_ _ _ _ _ _ _ _ _ _ _ performs multidimensional clustering in 2 steps.
a. CLIQUE
b. Wave cluster
c. COBBWEB
d. Outlier analysis
_ _ _ _ _ _ _ _ _ _ _ _ is a signal processing technique that decomposes a signal into different
frequency sub bands.
a. Wavelet transform
b. Outlier analysis
c. Outlier mining
d. SOMs
135.
136.
137.
138.
139.
140.

_ _ _ _ _ _ _ compete in a "winner-take-all" fashion for the object that is currently presented to
the system.
a. supervised learning
b. unsupervised learning
c. competitive learning
133.

a.
b.
c.
d.
142.
calculating outliers
mining outliers
classification outliers
detecting outliers
If there are 'm' number of objects within d-neighborhood of an outlier and later it is decided as
not an outlier because of its _ _ _ _ _ _ _ _ _ number of neighbors.
a. m
b. (m-1)
c. (m+1)
d.
143.
_ _ _ _ _ _ _ _ _ doesn't require a metric distance between the objects.
a. Exception set
b. dissimilarity f unction
c. cardinality function
d. smoothing factor
Outlier detection and outlier analysis is a data mining task referred to as _ _ _ _ _
a. data mining
b. Outlier mining
c. task mining
d. Analyzer mining
Data which are inconsistent with the remaining set of data is called as _ _ _ _ _ 
a. metadata
b. Outliers
c. procedures
d. process
_ _ _ _ _ _ _ _ _ _ is used to identify outliers w.r.t the model.
a. outlier test
b. discordancy test
c. differentiation test
d. integration test
_ _ _ _ _ _ _ _ _ _ _ verifies whether an object is significantly large or small in relation to the
distribution F.
a. outlier test
b. discordancy test
c. differentiation test
d. integration test
The _ _ _ _ _ _ _ _ states that discordant values are not outliers in distribution F, but
contaminants from some other distribution G.
a. Inherent alternative distribution
b. mixture alternative distribution
c. slippage alternative distribution
d. individual alternative distribution
It is difficult to construct an object cube containing _ _ _ _ _ _ _ _ _ _ dimension
a.generalization
b. keyword
c.plan base
d. pattern
Which of the following are spatial operators?
i)spatial-union
ii)spatial-overlapping
iii)spatial-intersection
iv)spatial-disjoint
a. i & ii
b. i & iv
c. i,ii,iii
d. i,ii,iv
144.
145.
146.
147.
148.
149.
150.
Block and consecutive procedures are 2 basic types for _ _ _ _ _ _ _ _ _ _ _
141.

a.
b.
c.
d.
152.
meta cube
hyper cube
object cube
grid cube
_ _ _ _ _ _ _ _ _ is a task of mining significant patterns from a plan base.
a. plan classification
b. plan detection
c. plan mining
d. plan construction
153.
Each object in a class is associated with _ _ _ _ _ _ _ _ _ _ _
a. object identifier & object name
b. object identifier & set of attributes
c. object name & set of attributes
d. object code & object type
A set-valued attribute may be _ _ _ _ _ _ _
i) homogenous
ii)heterogeneous
a. only i
b. only ii
c. i or ii
d. i and ii
_ _ _ _ _ _ _ _ and _ _ _ _ _ _ _ _ _ are important means of generalization .
a. attribution, aggregation
b. aggregation, approximation
c. approximation, analyzation
d. analyzation, attribution
A _ _ _ _ _ _ _ _ _ _ has complex tasks, graphics, images, videos, maps, voice, music etc.
a. meta data
b. multimedia database
c. MS-Access
d. SQL
_ _ _ _ _ _ _ _ is one where properties can be inherited from more than one super class.
a. multilevel inheritance
b. polymorphism
c. multiple inheritance
d. single inheritance
In
a.
b.
c.
d.
_ _ _ _ _ _ _ _ _ signature, its image includes a composition of multiple features.
color histogram based
multi feature composed
wavelet based
region based granularity
154.
155.
156.
157.
158.

.
159.
_ _ _ _ _ _ _ _ is a optimization method for spatial association analysis.
a. progressive regression
Each attribute to simple-valued data for constructing a multi-dimensional data cube are called as
___________
151.

b.
c.
d.
160.
progressive refinement
progressive coverage
refinement property
_ _ _ _ _ _ _ _ _ stores and manages a large collection of multimedia objects.
a. multimedia database system
b. database management system
c. relational database management system
d. graphical database system
A huge amount of space-related data are in _ _ _ _ _ _ _ _ _ forms.
a. images
b. ER Diagrams
c. Use case diagrams
d. Audio
_ _ _ _ refers to the extraction of knowledge spatial relationships not explicitly stored in spatial
databases.
a. spatial data mining
b. spatial data warehouse
c. spatial data representation
d. spatial data knowledge representation
_ _ _ _ _ _ _ _ _ is a dimension whose primitive level data are spatial starting at a certain level,
becomes non-spatial.
a. non-spatial dimensions
b. spatial to non-spatial dimensions
c. spatial to spatial dimensions
d. non-spatial to spatial dimensions
_ _ _ _ _ _ _ _ _ _ is a collection of pointers to spatial objects.
a. numerical measures
b. pointers measure
c. spatial measures
d. non-spatial measures
MBR stands for _ _ _ _ _ _ _ _ _ _ _ which represents 2 points for rough estimation of a merged
region.
a. memory buffer register
b. maximum bounding rectangle
c. minimum bounding rectangle
d. minimum bounding region
Consider a spatial association rule Is-a(X,"office") Λ near-by(X,"house") => near-by
(X,"university") [ 0.5 % 80 %] The rule states _ _ _ _ _ _ _ _ percent of the offices are close to
houses.
a. 0.5 %
b. 80 %
c. 80.5 %
d. 79.5 %
Precision= _ _ _ _ _ _ _ _ _ _
161.
162.
163.
164.
165.
166.
167.
Consider the data Original data: 3 7 2 0 7 2
Moving average of order 3: 4 3 2 6
Weighted(3,4,3) The first weighted average value is _ _ _ _ _ _ _ _ _ _ _
a. 4.3
b. 5.5
c. 6.3
d. 7.7

168.
_ _ _ _ _ _ _ _ refer to the cycles which are long term oscillations about a trend line/curve which
may/may not be periodic.
a. long term movements
b. cyclic movements
c. seasonal movements
d. random movements
If the time interval "int=0" means _ _ _ _ _ _ _ _ _ _
a. no interval gap is allowed
b. gap is allowed after 0 seconds
c. time interval=0
d. sequence of time slots doesn't exists
Mining _ _ _ _ _ _ _ _ _ _ specifies the periodic behavior of the time series at some, but not all of
the points in time.
a. full periodic pattern.
b. partial periodic pattern.
c. minimum periodic pattern.
d. periodic association rules
Time series analysis is also referred to as _ _ _ _ _ _ _ _ _
a. decomposition
b. time-analyzer
c. series-analyzer
d. decentralized
_ _ _ _ _ _ _ _ is the database that consists of sequence of ordered events with / without concrete
notions of time.
a. multimedia database
b. sequence database
c. concrete database
d. relational database
DFT and DWT are two popular data-independent transformations where F stands for _ _ _ _ _ _ _
_____
a. faster
b. freedom
c. Fourier
d. frequency
_ _ _ _ _ _ _ _ _ find all pairs of gap free windows of a small length that are similar.
a. atomic matching
b. window stitching
c. sub-sequence ordering
d. context switching
Web linkage structures, web contents etc., are included in _ _ _ _ _ _ _ _ _
a. data mining
b. text mining
c. web mining
d. multimedia mining
169.
170.
171.
172.
173.
174.
175.