Download COMP5331

COMP5331
Knowledge Discovery in Databases
Overview
Prepared by Raymond Wong
Presented by Raymond Wong
raywong@cse
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Course Details
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Reference books/materials:
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Papers
Data Mining: Concepts and Techniques.
Jiawei Han and Micheline Kamber. 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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Area
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DB or AI
This course can count towards one of
the areas ONLY and cannot be double
counted towards the required credits
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Course Details
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Grading Scheme:
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Assignment 30%
Project 30%
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 corrected 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
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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 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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Each project is completed by a group.
The number of students in a group
depends on the class size.
The duration of each presentation
depends on the class size.
It will be announced soon.
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Project
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Project Type (One of the following)
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Survey
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Implementation-oriented Project
Your group only needs to
read about 2~5 papers
Your group only needs to
read about 1~2 papers
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Research-oriented Project
You can read some papers
and conduct research
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Project
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Project Type (One of the following)
1.
2.
3.
Proposal
Presentation
Final report
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Survey
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Implementation-oriented Project
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Research-oriented
1.
2.
3.
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1.
2.
Project 3.
4.
Full Score = 80%
Full Score = 90%
Proposal
Presentation
Final report
Coding
Proposal
Full Score = 100%
Presentation
Final report (containing your
proposed methodology)
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Coding (if any)
Project
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Project Topic
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Some pre-selected topics/papers
Your own choice
For fairness, please do not choose the
topic which is closely related to your
own research
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Exam
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You are allowed to bring a calculator
with you.
Please remember to prepare a
calculator for the exam
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Major Topics
1.
2.
3.
4.
5.
6.
7.
Association
Clustering
Classification
Data Warehouse
Data Mining over Data Streams
Web Databases
Multi-criteria Decision Making
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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
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Milk
1
{Apple, Orange}
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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.
7.
Association
Clustering
Classification
Data Warehouse
Data Mining over Data Streams
Web Databases
Multi-criteria Decision Making
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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.
7.
Association
Clustering
Classification
Data Warehouse
Data Mining over Data Streams
Web Databases
Multi-criteria Decision Making
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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.
7.
Association
Clustering
Classification
Data Warehouse
Data Mining over Data Streams
Web Databases
Multi-criteria Decision Making
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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.
7.
Association
Clustering
Classification
Data Warehouse
Data Mining over Data Streams
Web Databases
Multi-criteria Decision Making
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5. Data Mining over Static
Data
1. Association
2. Clustering
3. Classification
Static
Data
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Output
(Data Mining Results)
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5. Data Mining over Data
Streams
1. Association
2. Clustering
3. Classification
…
Unbounded Data
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Output
(Data Mining Results)
Real-time Processing
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Major Topics
1.
2.
3.
4.
5.
6.
7.
Association
Clustering
Classification
Data Warehouse
Data Mining over Data Streams
Web Databases
Multi-criteria Decision Making
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6. Web Databases
Raymond Wong
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How to rank the webpages?
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Major Topics
1.
2.
3.
4.
5.
6.
7.
Association
Clustering
Classification
Data Warehouse
Data Mining over Data Streams
Web Databases
Multi-criteria Decision Making
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7. Multi-criteria Decision
Making
Suppose we want to look for a hotel which is close to a beach.
3 hotels
Hotel
Price
Distance to beach (km)
a
1000
4
b
2400
5
c
3000
1
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Suppose we compare hotel
a and hotel b
We know that hotel a is “better”
than hotel b
because
1. Price of hotel a is smaller
2. Distance of hotel a is smaller
We have two attributes.
Which hotel should we
select?
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7. Multi-criteria Decision
Making
Suppose we want to look for a hotel which is close to a beach.
3 hotels
Hotel
Price
Distance to beach (km)
a
1000
4
b
2400
5
c
3000
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Suppose we compare hotel
a and hotel c
We cannot determine hotel a is “better”
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than hotel c (wrt two attributes).
We cannot determine hotel c is “better”
than hotel a (wrt two attributes)..
This is because
1. Price of hotel a is smaller
2. Distance of hotel c is smaller
We have two attributes.
Which hotel should we
select?
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