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Title: Data%20Mining%20and%20Knowledge%20Acquizition%20%20

1
Data Mining and Knowledge Acquizition Chapter
7 Data Mining Overviewand Exam Questions
• 2014/2015 Summer

2
Outline
• Methodology - Overview
• Introduction
• Data Description Preprocessing
• OLAP
• Clustering
• Classification
• Numerical Prediction - Regression
• Frequent Pattern Mining
• Recent BIS Exams
• Unclassified Questions

3
Methodology and Overview
• KDD Methodology
• Functionalities

4
KDD Methodology
• Methodology
• Problem definition
• Data set selection
• Preprocessing transformations
• Functionalities
• Classification/numerical prediction
• Clustering
• Frequent Pattern Mining
• Association
• Sequential analysis
• others

5
KDD Methodology (cont.)
• Algorithms
• For classification you can use
• Decision trees ID3,C4.5 CHAID are algorithms
• For clustering you can use
• Partitioning methods k-means,k-medoids
• Hierarchical AGNES
• Probabilistic EM is an algorithm
• Presenting results
• Back transformations
• Reports
• Taking action

6
Data Description
• Single variables
• Categorical - Ordinal, nominal
• Frequency plots, tables, Pie charts
• Continuous interval, ratio
• Examine the probability distribution
• For two variables
• Both categorical
• Cross tabulation
• One categorical the other continuous
• Both are continuous
• correlation coeficient, scatter plots

7
Preprocessing
• Missing values
• Inconsistencies
• Redundent data
• Outliers
• Data transformations
• Data reduction
• Attribute elimination
• Attribute combination
• Samplinng
• Histograms

8
Functionalities
• Styles of Data Mining
• Descriptive - OLAP
• Classification
• Numerical Prediction
• Clustering
• Frequent Pattern Mining

9
Two basic style of data mining
• Descriptive
• Cross tabulations,OLAP,attribute oriented
induction,clustering,association
• Predictive
• Classification,numerical prediction
• Difference between classification and numerical
prediction
• Questions answered by these styles
• Supervised v.s. Unsupervised

10
Descriptive - OLAP
• Concept of data cube
• Fact table
• Measures calculated measures
• Keys
• Dimensions
• Sheams
• Star, snowflake
• Concept hierarchies
• Set grouping such as price age
• Parent child
• Attributes not suitable for concept hierarcies

11
Classification
• Methods
• Decision trees
• Neureal networks
• Bayesian
• K-NN or model based reasoning
• Given a problem which data processing techniques
are required
• Given a problem shich classification method or
algorithm is more apprpriate

12
Classification (cnt.d)
• Accuracy of the model
• Measures for classification/numerical prediction
• How to better estimate
• Holdout,cross validation, bootstraping
• How to improve
• Bagging, boosting
• For unbalanced classes
• What to do with models
• Lift charts

13
Numercal Prediction
• Learning is supervised
• Output variable is continuous
• Methods
• Regression
• Simple
• Multiple
• Most methods for classification can be used for
numerical prediction as well
• Accuricy
• Root mean square, absolute mean deviation

14
Clustering
• Distance measures
• Dissimilarity or similarity
• For different type of variables
• Ordinal,binary,nominal,ratio,interval
• Why need to transform data
• Partitioning methods
• K-means,k-medoids
• Hierarchical
• Density based
• probablistic

15
Frequent Pattern Mining
• Association analysis
• Apriori or FP-Growth
• How to measure strongness of rules
• Support and confidence
• Other measures of interestingness critique of
support confidence
• Multiple levels
• Constraints
• Sequential pattern mining

16
Outline
• Methodology - Overview
• Introduction
• Data Description Preprocessing
• OLAP
• Clustering
• Classification
• Numerical Prediction - Regression
• Frequent Pattern Mining
• Recent BIS Exams
• Unclassified Questions

17
Introduction
• Defineing problems
• Given a short description of an environment,
deine data mining problems fiting to different
functionalities, possible preprocessing problems
paciliur to the environment
• Basic functionalities
• Given a short description of a data mining
problem, with which functionality the problem is
solved?

18
Big University Library
• 1. Suppose that a data warehouse for
Big-University Library consists of the following
three dimensions users, books, time, and each
dimension has four levels not including the all
level. There are three measures You are asked to
perform a data mining study on that warehouse (25
pnt)
• Define three data mining problems on that
warehouse involving association, classification
and clustering functionalities respectively.
Clearly state the importance of each problem.
What is the advantage of the data being organized
as OLAP cubes compared to relational table
organisation?

19
Big University Library (cont.)
• In data preprocessing stage of the KDD
• What are the reasons for missing values? and How
do you handle them?
• what are possible data inconsistencies
• do you make any discritization
• do you make any data transformations
• do you apply any data reduction strategies

20
Big University Library (cont.)
• Define your target and input variables in
classification. Which classification techniques
and algorithms do you use in solving the
• Define your variables indicating their categories
in clustering Which clustering techniques and
algorithms do you use in solving the clustering
• Describe association task in detail specifying
the algorithm interestingness measures or
constraints if any.

21
Data mining on MIS
• A data warehouse for the MIS department consists
of the following four dimensions student,
course, instructor, semester and each dimension
has five levels including the all level. There
are two measures count and average grade. At the
of a student. You are asked to perform a data
mining study on that warehouse (25 pnt)

22
Data mining on MIS (cont.)
• Define three data mining problems on that
warehouse involving association, classification
and clustering functionalities respectively.
Clearly state the importance of each problem.
What is the advantage of the data being organized
as OLAP cubes compared to relational table
organisation?
• In data preprocessing stage of the KDD
• What are the reasons for missing values? and How
do you handle them?
• what are possible data inconsistencies
• do you make any discritization
• do you make any data transformations
• do you apply any data reduction strategies

23
Data mining on MIS (cont.)
• Define your target and input variables in
classification. Which classification techniques
and algorithms do you use in solving the
• Define your variables indicating their categories
in clustering Which clustering techniques and
algorithms do you use in solving the clustering
• Describe association task in detail specifying
the algorithm interestingness measures or
constraints if any.

24
Outline
• Methodology - Overview
• Introduction
• Data Description Preprocessing
• OLAP
• Clustering
• Classification
• Numerical Prediction - Regression
• Frequent Pattern Mining
• Recent BIS Exams
• Unclassified Questions

25
Data Description
• How to describe single variables categorical
and continuous
• How to desribe two association between two
variables
• bnoth continuous
• both categorical
• One continous, one categorical

26
Preprocessing
• What to do as preprocessing?
• Which techniques are applied?
• For what reason?

27
MIS 542 Midterm 2011/2012 Fall PCA
• 5. (10 points) Consider two continuous variables
X and Y. Generate data sets
• a) where PCA (principle component analysis) can
not reduces the dimensionality from two to one
• b) where although the two variables are related
(a functional relationship exists between these
two variables), PCA is not able to reduce the
dimensionality from two to one

28
MIS 542 Final 2011/2012 Falloutliers
• 1 (20 points) Give two examples of outliers.
• a) Where outliers are useful and essential
patterns to be mined.
• b) Outliers are useless steaming from error or
noise.

29
MIS 542 Final 2011/2012 Fall transformations
• 2 (20 points) Considering the classification
methods we cover in class, describe two distinct
reasons why continuous input variables have to be
normalized for classification problems(each
reason 10 points).

30
Outline
• Methodology - Overview
• Introduction
• Data Description Preprocessing
• OLAP
• Clustering
• Classification
• Numerical Prediction - Regression
• Frequent Pattern Mining
• Recent BIS Exams
• Unclassified Questions

31
OLAP
• Concept of data cube
• Fact table
• Measures calculated measures
• Keys
• Dimensions
• Sheams
• Star, snowflake
• Concept hierarchies
• Set grouping such as price age
• Parent child
• Attributes not suitable for concept hierarcies

32
Data warehouse for library
• A data warehouse is constructed for the library
of a university to be used as a multi-purpose
DSS. Suppose this warehouse consists of the
following dimensions user , books , time
(time_ID, year, quarter, month, week, academic
year, semester, day), and . Week is
considered not to be less than month. Each
academic semester starts and ends at the
beginning and end of a week respectively. Hence,
weekltsemester.
• Describe concept hierarchies for the three
dimensions. Construct meaningfull attributes for
each dimension tables above . Describe at least
two meaningfull measures in the fact table.
Each dimension can be looked at its ALL level as
well.
• What is the total number of cuboids for the
library cube?
• Describe three meaningfull OLAP queries and write
sql expresions for one of them.

33
Big University
• 2. (Han page 100,2.4) Suppose that the data
warehouse for the Big-University consists of the
following dimensions student,course,instructor,se
mester and two measures count and average_grade.
Where at the lowset conceptual level (for a given
student, instructor,course, and semester) the
the student. At higher conceptual levels the
given combination. (when student is MIS semester
2005 all terms, course MIS 541, instructor Ahmet
Ak, average_grade is the average of students
grades in thet course by that instructer in all
semester in 2005)

34
Big University (cont.)
• a) draw a snawflake sheam diagram for that
warehouse
• What are the concept hierarchys for the
dimensions
• b) What is the total nmber of cuboids

35
MIS 542 Final 2005/2006 Spring olap
• 1. MIS department wants to revise academic
strategies for the following ten years. Relevent
• questions are What portion of the courese are
required or elective? What is the full time part
• time distribution of instuctors? What is the
course load of instructors? What percent of
• technical or managerial courses are thought by
part time instructors? How all theses things

36
MIS 542 Final S06 1 cont.
• changed over years? You can add similar stategic
quustions of your own. Do not conside
• students aspects of the problem for the time
being. Desing and OLAP sheam to be used as a
• strategic tool. You are free to decide the
dimensions and the fact table. Describe the
concept
• hierarchies, virtual dimensions and calculated
members. Finally show OLAP opperations to
• answer three of such strategic questions

37
MIS 54 Final 2012/2013 Hospital
• 2. (20 pts) Suppose that a data warehouse for a
hospital consists of the following dimensions
time, doctor and patient and the two measures
count and charge, where charge is the fee a
doctor charge a patient for a visit.
• Design a warehouse with star schema
• a) Fact table Design the fact table.
• b) Dimension tables For each dimension show a
reasonable concept hierarchy.
• c) State two questions that can be answered by
that OLAP cube.
• d) Show drilldown and roll up operations related
to one of these questions

38
Humman Resource cube
• 1. (25 points) In an organization a data
warehouse is to be designed for evaluating
performance of employees. To evaluate performance
of an employee, survey questionnaire is
consisting a set of questions with 5 Likered
scale are answered by other employees in the same
company at specified times. That is, performance
of employees are rated by other employees.
• Each employee has a set of characteristics
including department, education, Each survey is
conducted at a particular date applied to some of
the employees. Questions are aimed to evaluate
broad categories of performance such as
motivation, cooperation ability,
• Typically, a question in a survey, aiming to
measure a specific attitude about an employee is
evaluated by another employee (rated f rom 1 to
5) Data is available at question level.

39
Human resource cube (cont.)
• Cube design a star schema
• Fact table Design the fact table should contain
one calculated member. What are the measures and
keys?
• Dimension tables Employee, and Time are the two
essential dimensions include a Survey and
Question dimensions as well. For each dimension
show a concept hierarchy.
• State three questions that can be answered by
that OLAP cube.
• Show drilldown and role up operations related to
these questions

40
MIS Midterm 2008/2009 Spring Shipment
• 1. (20 points) Consider a shipment company
responsible for shipping items from one location
to another on predetermined due dates. Design a
star schema OLAP cube for this problem to be used
by managers for decision making purposes. The
dimensions are time, item to be shipped, person
responsible for shipping the item, location.. For
each of these dimensions determine three levels
in the concept hierarchy. Design the fact table
with appropriate measuresand keys (include two
measure and at least one calculated member in the
fact table)
• Show one drilldown and role up operations
• Show the SQL query of one of the cuboids.

41
Outline
• Clustering

42
Outline
• Methodology - Overview
• Introduction
• Data Description Preprocessing
• OLAP
• Clustering
• Classification
• Numerical Prediction - Regression
• Frequent Pattern Mining
• Recent BIS Exams
• Unclassified Questions

43
Comparing clustering methods
• Clustering methods
• Partitioning, hierarchical, density based,
model-based probabnlistic EM
• Compare clustering methods
• Output
• Interpreteation
• Sensitivity ot aoutliers
• Speed of computation

44
clustering
• Construct simple data sets showing the
inadequacies of k-means clustering (20 pnt)
• this algorithm is not suitable of even spherical
clusters of different sizes
k-means

45
clustering
• Consider a delivery center location decision
problem in a city where a set of related products
are to be delivered to markets located in the
city. Design an algortihm for this lacation
selection problem extending an algortihm we cover
in class. State clearly the algorithm and its
extensions.for this particular problem.

46
Clustering preferences
• Consider a popular song competition. There are N
competitors A1, A2, AN. Number of voters is
very large a substantial fraction of the
population of the country. Each voter is able to
rank the competitors form best to worst e.g. for
voter 1 (A4gtA2gtA3gtA1) meaning that there are four
competitors and A4 is the best for voter 1 A1
being the worst. Suppose preference data is
available for a sample of n voters at the
beginning of competition.
• Develop a distance measure between the
preferences of two voters i and j
• Suppose you have the k-means algorithm available
in a package. Describe how you can use the
k-means algorithm to clusters voters according
to their preferences.

47
MIS 542 Final 2005/2006 Spring
• 3. a) Describe how to modify k-means algorithm so
as to handle categorical variables (binary,
ordinal, nominal).
• b) What is a disadventage of Agglomerative
hierarchical clustering method in the case of
large data. Suggest a way of eliminating this
agglomerative methods

48
MIS 542 Midterm 2007/2008 Spring
• Generate data set of two continuous variables X
and Y. Consider clustering based on density
• When clustered with one variable there (either X
or Y) there is one cluster
• When clustered with both variable there there are
two clusters

49
MIS 542 Final 2011/2012 Fall
• 3 a (10 points) Generate data sets for two
clustering problems with two continuous
variables. Two natural clusters for the notion of
density based clustering but the quality of
these clusters are low for a partitioning
approach based on dissimilarity such as k-means
• 3.b (10 points) Considering the advantages and
agglomerative clustering approaches. Design a
method for combining the two approaches to
improve good clustering quality. (Finally there
are hierarchies of clusters)

50
MIS Midterm 2011/2012 Fall
• 6. (25 points) A retail company asked to segment
its customers. Following variables are available
for each customer age, income, gender number of
children, occupation, house owner, have a car or
not. There are 6 category of goods sold by the
company and total purchases from each category is
available for each customer, in addition average
• inter-purchase time is also included in the
database.

51
MIS Midterm 2011/2012 Fall
• a) What are the types and scales of these
variables?
• b) If your tool has only k-means algorithm which
of these variables are more suitable for the
segmentation problem?
• c) What data transformations are to be applied?
• d) How do you reduce number of variables used in
the analysis?
• e) If you want to include categorical variables
into your clustering, how would you treat them?

52
Midterm 2011/2012 Fall
• In Question 3-5 artificial data sets are
generated for given situations.
• 3. (10 points) Consider a data set of two
continuous variables X and Y. There are two
clusters (k2)
partitioning methods k-means and k-medoids of
clustering, generate two dimensional data set
• a) (5 pnt) Produces almost the same clusters by
k-medoids and k-means
• b) (5 pnt) Produces different clusters by
k-medoids and k-means

53
Outline
• Methodology - Overview
• Introduction
• Data Description Preprocessing
• OLAP
• Clustering
• Classification
• Numerical Prediction - Regression
• Frequent Pattern Mining
• Recent BIS Exams
• Unclassified Questions

54
Outline
• Classification
• General
• Decision trees
• Neural networks
• Bayesian
• K-NN
• Accuricy Measures

55
Information gain
• Consider a data set of two attributes A and B. A
is continuous, whereas B is categorical, having
two values as y and n, which can be
considered as class of each observation. When
attribute A is discretized into two equiwidth
intervals no information is provided by the class
attribute B but when discretized into three
equiwidth intervals there is perfect information
provided by B. Construct a simple dataset obeying
these characteristics.

56
Decision tree
• 2. a-Construct a data set that generates the tree
shown below In addition the following conditions
are satisfied

57
MIS 541 2012/2013 Final
• 1. (20 pts) Consider a decision tree with only
two branches in that the attribute selection
measure is entropy. Bearing in mind that each
candidate input attribute may have more then two
distinct values, how do you modify the ID3
algorithm to handle such a constraint on the
number of branches of the tree.

58
MIS 542 Final 2005/2006 Spring
• 2. Given the training data set with missing
values
• A(Size) B(color) C(shape) Class
• small yellow round A
• big yellow round A
• big yellow red A
• small red round A
• small black round B
• big black cube B
• big yellow cube B
• big black round B
• small yellow cube B

59
MIS 542 Final 2005/2006 Spring (cont.)
• a) Apply the C4.5 algorithm to construct a
decision tree.
• b) Given the new inputs Xsize small,color
missing, shaperound.and Ysize big,color
yellow, shapemissing What is the prediction of
the tree for X and Y?
• c) How do you classify the new data points given
in part b) using Bayesian Classification?
• d) Analyse the possibility of pruning the tree.
You can make normal approximation to Binomial
distribution though number of observations is
low. z value for upper confidence limit of c25
is 0.69.

60
MIS 542 Final S06 neural networks
• 4. Consider a classification problem with two
classes as C1 and C2. There are two numerical
input variables X1 and X2, taking values
between 0 and infinity. All observations are of
class C1, if they are above X2 1/X1 curve (a
hyperbola) All other observations are class C2.
Describe how multilayer perceptrons can separate
such a boundary using as few hidden nodes as
possible.

61
MIS 542 Midterm S08 2 csass,f,cat,pm
• Consider a clasification problem with two
continuous variables X and Y and a categorical
output with two distinct values C1 and C2
• Generate data set such that
• A) Decision trees are appropriate for
clasification
• B) Decision trees are not appropriate for
clasification but a perceptron can classify the
data succesfully
• C) Even s single perceptron is not enough to
classify the data
• D) How do you encorporate a perceptron into
decision trees so that cases in B and C can be
clasified by an hybrid approach of DTs and
perceptron

62
Final 2010/2011 Spring
• 2 (30 pt.) Consider a prediction problem e.g.
predicting weight using height(a continuous
variable) as input, solved by neural networks.
Such methods as back propagation try to minimize
the prediction error but it is claimed that the
magnitude of error depends on the weight a
prediction error of 0.5 for a baby with a short
height should not be the same as for an adult
with a height of 2.00 meters.
• a) Make a scatter plot of such a hypothetical
data set for a two variable problem.
• b) Plot the prediction error on another graph
• c) Do you need to modify the back propagation
algorithm so as to handle such a situation? If so

63
Final 2011/2012 Fall pverf,tt,mg
• 4. Illustrate the over fitting of neural networks
for the following cases by generating data sets.
• a) (10 points) For a binary classification
problem with two continuous inputs.
• b) (10 points) For a numerical prediction problem
(output being continuous) with one continuous
input variable.

64
Midterm 2011/2012 Fall
• 4. (10 points) Consider a classification by a
decision tree problem. Consider a categorical
input variable A having two distinct values. The
output variable B has two distinct classes as
well. At a particular node of the tree there are
N data objects. Generate partitioning of data by
input variable A for the following
• a) A does not provide any information does not
decrease information gain at all.
• b) A does provides perfect information decrease
information gain as much as possible

65
MIS 541 2012/2013 Final
• 5. (20 pts) Consider a classification problem
solved by k-NN. Suppose in your dataset all
inputs are continuous variables. Why do you need
to apply any data transformations? What data
transformation is applied? Suppose the variables
are to be weighted after transformations. Device
a method for determining optimal weights for
variables s well as determining optimal k value
considering that k-NN is a supervised learning
method.

66
MIS 541 2012/2013 Final
• 5..(20 pts) The follwing table consists of
training data from an employee database.
• Predicted variable is status. Age,Salary and
Department are inputs
• Design a multilayer feedforward neural network
for the given data. Label the noedes in the
input, hidden and output layers. Describe how
you encode the input and output variables,
specifiy the parameters of the network that can
be changed by the backpropegation algorithm.

67
Department Status Age Salary
Sales Senior 31-35 46K-50K
Sales Junior 26-30 26K-30K
Sales Junior 31-35 31K-35K
Systems Junior 21-25 46K-50K
Systems Senior 31-35 66K-70K
Systems Junior 26-30 46K-50K
Systems Senior 41-45 66K-70K
Marketing Senior 36-40 46K-50K
Marketing Junior 31-35 41K-45K
Secretary Senior 46-50 36K-40K
Secretary Junior 26-30 26K-30K
68
Accuracy measures
• For class balanjcy or unbalancy problems
• Output variables with ordinary scale
• How do you modify the accuricy measure for an
ordinal output variable with three different
values
• Give an example for such a variable

69
Outline
• Methodology - Overview
• Introduction
• Data Description Preprocessing
• OLAP
• Clustering
• Classification
• Numerical Prediction - Regression
• Frequent Pattern Mining
• Recent BIS Exams
• Unclassified Questions

70
BIS 541 2012/2013 Final II
• 5. Based on a sample of 30 observations the
population regression model
• Y i ?0 ?1x i ?i
• The least square estimates of intercept is 10.0
• Sum of the values of dependent and independent
variables are 450 and 150 respectively.
• Estimated variance of dependent variable is 25,
variance of the residuals is 4
• a) What is the least square estimate of slope
coefficient? Interpret the figure.
• b) What are the values of SSR and SSE?
• c) Find and interpret the coefficient of
determination.
• d) Test the null hypothesis that the explanatory
variable X does not have a significant effect on
Y at confidence level of 95.Critical value of
F?0.05(1,28) 4.20

71
BIS 541 2013/2014 Final
• 4. Based on a sample of 50 observations the
population regression model to predict number of
automobile sales (dependent variable) based on
• Y i ?0 ?1x i ?i
• The least square estimates of slope is 2.0
• Average of the values of independent variable is
50. Sum of the values of dependent variable is
5390.
• Total sum of squares for dependent variable is
9000 Variance of the residuals is 40

72
BIS 541 2013/2014 Final
• a) What is the least square estimate of intercept
coefficient? Interpret the figure.
• b) Interpret the the slope coefficient.
• b) What are the values of SSR and SSE?
• c) Find and interpret the coefficient of
determination.

73
MIS 214 Midterm 2012/2015 Summer
• 5. (20 pt) An analyst want to estimate
dependence of quantity demanded of a product (Y)
on its price (X1) and price of its substitute
(X2) using linear regression, based on a large
sample of data obtained from 50 weeks
• Fill the missing parts in the following
regression outputs (From a to l this letter l)
• Do not report the s but you may need their
values.
• Do not write on this table
• R-square f
• Standard error of regression h
• SS d.f. MS F p-value
• Regression a c d e
• Error b d 2.5
• Total 400 e

74
MIS 214 Final 2013/2014 Spring
• 1 (20 pt) For the following four scenarios, each
having two cases denoted by I and II, draw
scatter plots of X (explanatory variable) and Y
(dependent variable) showing the population
regression model drawn as a line or curve as
well. Use around 20-25 hypothetical points unless
otherwise stated assumptions of least square are
hold. In I and II population slope and intercepts
are the same
• a) In II variance of the error is higher than
in I.
• b) In II coefficient of determination is
higher than in I.
• c) In II spread of X is higher than in I.
• d) In II variance of the error term increases
with higher values of X.. In I, variance of error
is homoscedastic.

75
Outline
• Methodology - Overview
• Introduction
• Data Description Preprocessing
• OLAP
• Clustering
• Classification
• Numerical Prediction - Regression
• Frequent Pattern Mining
• Recent BIS Exams
• Unclassified Questions

76
Exercise
• a) Suppose A ? B and B ? C are strong rules
• Dose this imply that A ? C is also a strong rule?
• b) Suppose A ? C and B ? C are strong rules
• Dose this imply that A AND B ? C is also a strong
rule?
• c) Suppose A ? B and A ? C are strong rules
• Dose this imply that A ? B AND C is also a
strong?
• d) Suppose A ? B AND C is a strong rule. Dose
this imply that A ? B and A ? C are strong rules?
• e) Suppose A AND B ? C is a strong rule. Dose
this imply that A ? C and B ? C are strong rules?

77
Exercise
• a) Suppose A,B,C is a frequent 3 itemset. Dose
it imply that A,B and A,C are frequent 2
itemsets?
• b) Suppose A,B, A,C, and B,C are frequent 2
itemsets. Dose it imply that A,B,C is a
frequent 3 itemset?
• c) Suppose A,B is a frequent 2 itemset. Dose it
imply that, A ? B and B ? A are strong rules?

78
Associations
• In a particular database A?C and B?C are
strong association rules based on the support
confidence measure. A and B are independent
items. Does this imply that A ? B?C is
also a strong rule based on the lift measure?
A,B,C are items in a transaction database.
• -if A ?B and B?C are strong. Is A?C a strong rule
• -if A ?B and A?C are strong. Is B?C a strong rule

79
MIS 542 midterm S06 association constratint
• The price of each item in a store is nonnegative.
For the following cases indicate the type of
constraints (such as monotone, untimonotone,
tough, storngly convertable or succinct)
• a) Containing at least one Nintendo Game.
• b) The average price of items is between 100 and
500.

80
BIS 541 2012/2013 Final II
• 4. The questions about constaint-based
association rule mining
• The price of each item is nonnegative For the
following cases indicate the type of constraints
(monotonic, anti-monotonic or none)
• a) the sum of prices of items is less then or
equal to 10
• b) the average price of items is less then or
equal to 20

81
MIS 214 Final 2013/2015 Spring
• (15 pt) Given that L4 (1,2,3,4),(2,4,5,6)where
1,2,...,6 are ID s of items.
• a) Write a L3 consisting of five 3-itemsets
• b) Write a C3 of seven 3-itemsets

82
Outline
• Methodology - Overview
• Introduction
• Data Description Preprocessing
• OLAP
• Clustering
• Classification
• Numerical Prediction - Regression
• Frequent Pattern Mining
• Recent BIS Exams
• Unclassified Questions

83
BIS 541 2011/2012 Final
• 1. For each of the following problem identify
• a) A weather analyst is interested in
calculating the likely change in temperatue for
the coming days.
• b) A marketing analyst is looking for the
groups of customers so as to apply different CRM
strategies for ecach group
• c) A medical doctor must decide whether a
set of symptoms is an indication of a particular
disease.
• d) A educational psychologist would like to
determine exceptional students to sugget them for
special educational programs. .

84
BIS 541 2011/2012 Final
• 2. Develop a data warehouse for an insurance
company using fact constellations scheme. The
company holds insurance premiums paind by its
customers for different type of policies as well
as the payments in case of accidents to its
customers. There are two facat tables for
dimensions are customer time, policy accident
some are sheered by the two fact tables.
• a) design the fact tables keys and measures
• b) design the dimension tables their concept
hierarchies
• c) show one roll up and one drill down opperation

85
BIS 541 2011/2012 Final
• 3. Consider a customer segmentation problem to
be solved with k-means algorithm. . The following
variables are available in the dataset gender,
member card information, total spending in TL and
education level.
• a) what are the scales of these variables.?
• b) How would you transform data before applying
clustering?
• c) How do you find similarity/dissimilarity
between two customers?

86
BIS 541 2011/2012 Final
• 4. Construct a particular node of a decision tree
There are 6 data points at that node. The output
is a categorical variable with two distinct
values. Generate a dtra set of three variables
one bieing the output (Y) the others are inputs
(X1 and X2) such that X1 reduces the information
gane as much as possible whereas X2 dose not
reduces the information gain at all.

87
BIS 541 2011/2012 Final
• 1. Generate two different data sets of two
continuous input variables X1 and X2 for a
clustering problem.
• a) that would give almost the same set of
clustering results when solved by k-means and
k-medoids
• b) that would give different set of clusters
when solved by k-means and k-medoids

88
BIS 541 2011/2012 Final
• 2. Develop a data warehouse for holding academic
performance of an universitys faculty members.
The dimensions are time (here academic year is
important but the day of the publication is a bit
detailed) faculty member, paper. For an article
publiched by a factulty member at a particular
paper, number of citations taken.and the implact
factor of that paper are important. Paper can be
journal articles, conference proceedings journals
can be in SCI or SSCI and each such ournal or
conference has a prestige factor a continous
variable.
• a) design the fact table keys and measures
• b) design the dimension tables their concept
hierarchies
• c) describe in word fife different types of
queries that can be answered by the OLAP cube
• d) show two roll up and two drill down operation

89
BIS 541 2011/2012 Final
• 3. Generate data sets for a supervised learning
problem solved by neural networks.
• a) There are two continuous independent
variables X1 and X2 and a class variable with two
different values such as yes and no. On the same
artificially generatred dataset illustrate the
concept of overfitting by neural networks.
• b) Illustrate the behavior of training and test
errors as the complexity of the network increases

90
BIS 541 2011/2012 Final
• 4. Consider a classification problem to be solved
by k-NN method. The output is whether the
customer will buy a product or not. The inputs
are income, age, education level of the customer
and profession of the customer (having here
distinct values)
• a) Describe the data transformations needed in
the preprocessing step to prepare the datra set
to be classified by k-NN
• b) How the data transformations are different
from the solution of th same problem by neural
networks.

91
BIS 541 2012/2013 Final II
• 1 For each of the following problem identify
relevant data mining tasks with a brief
explanation
• a) A weather analyst is interested in
wheather the temperature will be up or down for
the coming day
• b) An insurance analyst intends to group
policy holders according to characteristics of
customers and policies
• c) A medical researcher is looking for
symptoms that are occurring together among a
large set of pationes.
• d) An educational program director would like
to determine likely GPA of applicant to a MA
program from their ALES scores, undergraduate
GPAs and enterence exam scores.

92
BIS 541 2012/2013 Final II
• 2. Develop a data warehouse for a weather bureau
having so many probes located all over a large
region, using star scheme. These probes collect
basic weather data such as temperature , air
pressure , humidity, at each hour. All the data
is sent to a central station to be processed. .
• a) design the fact table keys and measures
• b) design the dimension tables their concept
hierarchies
• c) state two questions that can be answered by
querying the warehouse.
• d) show one roll up and one drill down operation
abour one of these questions

93
BIS 541 2012/2013 Final II
• Evaluate the four classification methods
decision threes, neural networks, Bayesian
classification and k-NN in terms of
• a) accuricy
• b) speed of model development and use
• c) understandability and interpretability of
output
• d) handling of outlayers if not handled in
preprocessing step

94
BIS 541 2012/2013 Final II
• 4. The questions about constaint-based
association rule mining
• The price of each item is nonnegative For the
following cases indicate the type of constraints
(monotonic, anti-monotonic or none)
• a) the sum of prices of items is less then or
equal to 10
• b) the average price of items is less then or
equal to 20

95
BIS 541 2012/2013 Final II
• 5. Based on a sample of 30 observations the
population regression model
• Y i ?0 ?1x i ?i
• The least square estimates of intercept is 10.0
• Sum of the values of dependent and independent
variables are 450 and 150 respectively.
• Estimated variance of dependent variable is 25,
variance of the residuals is 4
• a) What is the least square estimate of slope
coefficient? Interpret the figure.
• b) What are the values of SSR and SSE?
• c) Find and interpret the coefficient of
determination.
• d) Test the null hypothesis that the explanatory
variable X does not have a significant effect on
Y at confidence level of 95.Critical value of
F?0.05(1,28) 4.20

96
BIS 541 2013/2014 Final
• 1. For each of the following problem identify
relevant data mining tasks with a brief
explanation
• a) A financial analyst is interested in
wheather the stock market index will be up or
down for the coming day
• b) Cities in Turkey are grouped according to
their voting characteristics after the Republic
of President election.
• c) A security specialist is interested in
determining mail message are spam or no looking
at words passing the messages.
• d) A medical doctor is interested in what
symptoms (binary variables) occur together for a
specific gtype of canser.

97
BIS 541 2013/2014 Final
• 2. Evaluate the four clustering methods k-means,
k-medoids, hierarchical, model-based
(probalictic) in terms of
• a) handling of non-spherical shapes
• b) speed of model development
• c) understandability and interpretability of
output
• d) sensitivity to outlayers.
• In each of these aspects mention only the
remarkable methods (you need not mantion all
methods in all aspects)

98
BIS 541 2013/2014 Final
• 3. Develop a data warehouse for the election to
selection of president of republic. There are
so many poll stations (sandik) located all over
the country. Using star scheme.. Each pool
station has valid notes for each of the three
candidates, invalid ots and total number of
voters. Each poll station has a set of lacation
related variables such as district, city,.some
characteristics of cities There is no time
dimension in this version of the problem.

99
BIS 541 2013/2014 Final
• a) design a warehouse with star shame fact table
keys and measures and at least two calculated
measures.
• b) design the dimension tables their concept
hierarchies
• c) state two questions that can be answered by
querying the warehouse.
• d) show one roll up and one drill down operation
abour one of these questions

100
BIS 541 2013/2014 Final
• 4. Based on a sample of 50 observations the
population regression model to predict number of
automobile sales (dependent variable) based on
• Y i ?0 ?1x i ?i
• The least square estimates of slope is 2.0
• Average of the values of independent variable is
50. Sum of the values of dependent variable is
5390.
• Total sum of squares for dependent variable is
9000 Variance of the residuals is 40

101
BIS 541 2013/2014 Final
• a) What is the least square estimate of intercept
coefficient? Interpret the figure.
• b) Interpret the the slope coefficient.
• b) What are the values of SSR and SSE?
• c) Find and interpret the coefficient of
determination.

102
Outline
• Methodology - Overview
• Introduction
• Data Description Preprocessing
• OLAP
• Clustering
• Classification
• Numerical Prediction - Regression
• Frequent Pattern Mining
• Recent BIS Exams
• Unclassified Questions

103
• 5. (25 points) Consider a data set representing
the interactions among a set of people. The
degree of interaction is a positive real number
high values can be interpreted as, the two
members are closely related (they have close
interactions such as heavy telephone calls or
mail traffic between them) In other words rather
then including the coordinates of variables
directly, the similarity/dissimilarity matrix is
given. This is a symmetric matrix. Develop an
algorithm for clustering similar objects into
same clusters. Assume that number of clusters (k)
is given

104
• 3. (25 points) Consider a data set of two
continuous variables X and Y. X is right skewed
and Y is left skewed. Both represent measures
about same quantity (sales categories, exam
• a) Draw typical distributions of X and Y
separately.
• b) Draw box plots of X and Y separately.
• c) Draw q-plots (quantile) of X and Y
separately.
• d) Draw q-q plot of X and Y.

105
• 4. (25 points) A strategy for clustering high
dimensional data of continuous variables is
First apply principle components to reduce the
dimensionality of the data set and apply
clustering on the reduced form of the data.
Discuss the drawback(s) of this approach.

106
MIS 541 2012/2013 Final
• 1. (20 pts) Consider a data set of two continuous
variables X and Y. X both has the same mean, both
have no skewness (symetric)ç X has a higher
variance then Y. Both represent measures about
same quantity (sales categories, exam grades,)
• a) Draw typical distributions of X and Y on
the same graph.
• b) Draw box plots of X and Y separately.

107
MIS 541 2012/2013 Final
• 2. (20 pts) Illustrate with plots of two
continuous inputs and binary class that one layer
neural networks are enough to classify convex
class boundaries Two hidden layers are enough to
capture even non convex class boundaries.

108
MIS 541 2012/2013 Final
• 3. (20 pts) Consider association rules X ?Y where
X is a categorical variable with more then two
values and Y is originally continuous but
discretize into categories. Give example
variables for X and Y. Illustrate that confidence
as an interestingness measure may be misleading.
Suggest a modification to the classical
confidence so as to eliminate its drawback for
this type of variables.

109
MIS 541 2012/2013 Final
• 4. (20 pts) The price of each item is nonnegative
For the following cases indicate the type of
constraints (monotone, anti-monotone, tough,
strongly convertible or succinct)
• a) the sum of prices of items is less then or
equal to 10
• b) the average price of items is less then or
equal to 20

110
Midterm 2008/2009 Spring
• 2.(20) Consider a classification problem in that
customers that are taking consumer credits from a
bank are classified into three risk groups The
input variables are age discretized into 4
groups, income into 4 groups, education into four
groups, gender, number of months the customer is
dealing with the bank and average delay of
payments in months, and current value of the
accont balance. The output variable has 3
categories as risky, normal or highly risky
calculated by some procedure and provided to the
data miner. Design an encoding schema for the
input and output variables so that the problem
will be solved by a neural network Show a typical
topology of a feedforward network architecture

111
Midterm 2008/2009 Spring
• 3. (20 points) Consider a classification by a
decision three problem. There are two categorical
input variables A and B having two distinct
values each. The output variable C has two
distinct classes. Suppose the dataset is suitable
for using decision threes. Is the order of
selection of variables affects the
generating data sets pictorially. (stoping
condition is either a pure class is obtained or
no variables remains to be tested)

112
Midterm 2008/2009 Spring
• 4. (20 points) Principle components is used for
dimensionality reduction then may be followed by
cluster analysis say for segmentation purposes
Consider a two continuous variable problem.
Using scatter plots
• a) Generate a data set where PCA reduces the
dimensionality from two to one
• b) Generate a data set where although there is a
relation between the two variables, PCA
• is not able to reduce the dimensionality to one
• c) Generate a data set where there are natural
clusters and PCA can reduce the dimensionality
• d) Generate a data set where there are natural
clusters but PCA is not the appropriate method
for reducing the dimensionality