Download COMP1942

COMP1942
Exploring and Visualizing Data
Overview
Prepared by Raymond Wong
Presented by Raymond Wong
raywong@cse
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Course Details
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Instructor
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Dr. Raymond Wong
TA
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
Kai Ho CHAN
Dandan LIN
Junqiu WEI
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Course Details
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Webpage
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http://course.cse.ust.hk/comp1942/
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Course Details
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Lecture
 Time: Monday (1:30pm - 2:50pm) and
Friday (9:00am - 10:20am)
 Venue: G010 (CYT Building)
Tutorial will be announced via email.
Tutorial
 Time: Monday (12:30pm-1:20pm)
Venue: Room 5583 (LT 29-30) (Academic Building) or
CSE Lab 3 (Rm 4213 (Academic Building))
 Time: Tuesday (12:30pm-1:20pm)
Venue: Rm 2302 (LT 17-18) (Academic Building) or
CSE Lab 3 (Rm 4213 (Academic Building))
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Course Details
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Textbook
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Data Mining for Business Intelligence:
Concepts, Techniques, and Applications in
Microsoft Office Excel with XLMiner. Galit
Shmueli, Nitin R. Patel and Peter C. Bruce,
Wiley 2010 (2nd edition)
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Course Details
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Reference books/materials:
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Data Mining: Concepts and Techniques.
Jiawei Han, Micheline Kamber and Jian PEI.
Morgan Kaufmann Publishers (3rd edition)
Introduction to Data Mining. Pang-Ning
Tan, Michael Steinbach, Vipin Kumar
Boston : Pearson Addison Wesley (2006)
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Common Core Requirement
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My ability to use quantitative methods to
define, analyze and solve problems in daily life
has been enhanced.
I am more able to process quantitative data
and to use the data to reach a conclusion in a
logical way.
The course has aroused my interest in learning
more about mathematical models or
quantitative methods.
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Course Details
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Grading Scheme:
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Assignment 10%
Project 20%
In-class Participation 10%
Mid-Term Exam 20%
Final Exam 40%
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Assignment
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2 assignments
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Assignment 1
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Assignment 2
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Content before the mid-term exam
Content after the mid-term exam
NOTE: No late submissions are allowed.
Assignment 10%
Project 20%
In-class Participation 10%
Mid-Term Exam 20%
Final Exam 40%
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Assignment
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If the students can answer the selected
questions in class correctly,
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for each correct answer,
I will give him/her a coupon
This coupon can be used to waive one
question in an assignment
which means that s/he can get full marks for
this question without answering this question
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Assignment

Guideline
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For each assignment, each student can waive at most
one question only.
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s/he can waive any question s/he wants and obtain full marks
for this question (no matter whether s/he answer this
question or not)
s/he may also answer this question. But, we will also mark it
but will give full marks to this question.
When the student submits the assignment,
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please staple the coupon to the submitted assignment
please write down the question no. s/he wants to waive on
the coupon
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Project
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Phase 1 (Excel file)
Phase 2 (Design Report)
Phase 3 (Final Report and Output files)
Assignment 10%
Project 20%
In-class Participation 10%
Mid-Term Exam 20%
Final Exam 40%
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Project
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You are required to form a group.
Each group contains 1 or 2 members.
3-member group is NOT allowed.
Please fill in the following information of each member in the
link
https://goo.gl/forms/WguJdkHO8TkpOFYn1
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student ID
student name
Email
One group needs to submit the grouping information ONCE.
The group forming deadline is 15 Feb (Wed) 1pm.
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Project
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Data Mining Tool: XLMiner (in MS Excel)
Installed in CSE Lab 3 (Rm 4213)
All non-CSE students and all non-CPEG
students need to apply for the CSE
account.
You can see the details in our course
webpage.
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Project
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In Phase 3 (the last phase), you are
required to hand in some output files
We will check the output files
You can use at most one coupon to
obtain full marks for all output files
Each group can use at most one coupon
Please staple your coupon with your
final report.
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In-class Participation
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In each lesson, you are required to bring
one of the following with you.
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your smart phone installed with iPRS (Internet
enabled Personal Response System) or
your PRS device
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Assignment 10%
Project 20%
In-class Participation 10%
Mid-Term Exam 20%
Final Exam 40%
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In-class Participation
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If you have a smart phone (Android/iOS),
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please install an app called “HKUST iLearn” in
your smart phone (Android/iOS).
If you do not have a smart phone,
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you have to borrow your PRS device.
please visit ITSC Service Desk at Rm 2021 (Lift 2)
to borrow your PRS device.
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In-class Participation
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In each lesson, you may be asked
about some multiple-choice questions
(e.g., 1-3 questions)
You have to use your iPRS to answer
the questions
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In-class Participation
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You can obtain 1 unit for in-class
participation when you answer a
question in class with your iPRS (no
matter whether you answer it correctly
or not)
Those questions may be in the midterm exam and the final exam.
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In-class Participation
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In some cases,
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Some students may be absent for some
reasons in class
The iPRS system could not record your answer
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E.g., your smart device and the iPRS system crash
You are required to obtain 20 units in order to
obtain the full score (10%) for the in-class
participation
We will give at least 25 questions in the course.
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Midterm and Final Exam
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You are allowed to bring a calculator
with you.
Please remember to prepare a
calculator for the exam
Assignment 10%
Project 20%
In-class Participation 10%
Mid-Term Exam 20%
Final Exam 40%

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Midterm Exam
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In-class Midterm
Date: 17 March, 2017 (Fri)
Time: 9:00-10:20
Venue: G010 (CYT Building)
Rm 5619 (LT 31/32) (Academic Building)
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Major Topics

In this course, you are expected to
learn something related to “Exploring
and Visualizing Data”.
Not only this!

In this course, you are expected to
learn how to solve problems and how to
analyze problems.
This is very important to your future.
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Major Topics
1.
2.
3.
4.
5.
6.
Association
Clustering
Classification
Data Warehouse
Dimension Reduction
Web Databases
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1. Association
Customer
Apple
Orange
Raymond
Apple
Orange
Ada
Grace
Orange
Apple
Orange
…
…
…
Items/Itemsets
Frequency
Apple
2
Orange
3
Milk
1
{Apple, Orange}
2
{Orange,
Milk}
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Milk
Milk
We are interested in
the items/itemsets
with frequency >= 2
…
Frequent Pattern
(or Frequent Item)
Frequent Pattern
(or Frequent Item)
Frequent Pattern
(or Frequent Itemset)
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1. Association
Customer
Apple
Orange
Raymond
Apple
Orange
Ada
Grace
Orange
Apple
Orange
…
…
…
Items/Itemsets
Frequency
Apple
2
Orange
33
Milk
1
{Apple, Orange}
22
Milk
Milk
We are interested in
the items/itemsets
with frequency >= 2
Association Rule:
…Apple  Orange
1.
( 100% customers who buy
apple will probably buy orange.)
2. Orange  Apple
( 67%
customer who buy
orange will probably buy apple.)
Problem:
toMilk}
find all frequent
{Orange,
1 patterns and association rules
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Major Topics
1.
2.
3.
4.
5.
6.
Association
Clustering
Classification
Data Warehouse
Dimension Reduction
Web Databases
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2. Clustering
Raymond
Louis
Wyman
…
Computer
100
History
40
90
20
45
95
…
Cluster 2
(e.g. High Score in History
and Low Score in Computer)
History
…
Cluster 1
(e.g. High Score in Computer
and Low Score in History)
Computer
Problem: to find all clusters
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Major Topics
1.
2.
3.
4.
5.
6.
Association
Clustering
Classification
Data Warehouse
Dimension Reduction
Web Databases
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3. Classification
Suppose there is a person.
Race
Income
Child
Insurance
white
high
no
?
child=yes
root
child=no
100% Yes
0% No
Income=high
100% Yes
0% No
Income=low
0% Yes
100% No
Decision tree
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Major Topics
1.
2.
3.
4.
5.
6.
Association
Clustering
Classification
Data Warehouse
Dimension Reduction
Web Databases
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4. Warehouse
Query
Users
Databases
Need to wait for a long time
(e.g., 1 day to 1 week)
Databases
Data
Warehouse
Users
Pre-computed results
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Major Topics
1.
2.
3.
4.
5.
6.
Association
Clustering
Classification
Data Warehouse
Dimension Reduction
Web Databases
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Suppose we have the
following data set
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According to the data, we find the
following vectors (marked in red)
e1
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e2
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Consider that the data points are
projected on e1
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Suppose all data points are projected
on vector e1
e1
This corresponds to the
information loss
This corresponds to
another information loss
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e2
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After all data points are projected on
vector e1
e1
Thus, the total information loss is
small.
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e2
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We can use only 1 dimension to
represent all data points (i.e., vector e1)
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Major Topics
1.
2.
3.
4.
5.
6.
Association
Clustering
Classification
Data Warehouse
Dimension Reduction
Web Databases
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6. Web Databases
Raymond Wong
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How to rank the webpages?
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