CSC5120 Tutorial 1

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COMP537
Knowledge Discovery in Databases Overview
Prepared by Raymond Wong Presented by Raymond
Wong raywong_at_cse
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Course Details

• Reference books/materials
• Papers
• Data Mining Concepts and Techniques. Jiawei Han
  and Micheline Kamber. Morgan Kaufmann Publishers
  (2nd edition)
• Introduction to Data Mining. Pang-Ning Tan,
  Michael Steinbach, Vipin Kumar Boston Pearson
  Addison Wesley (2006)

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Area

• 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

• Grading Scheme
• Assignment 30
• Project 30
• Final Exam 40

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Assignment

• If the students can answer the selected questions
  in class correctly,
• 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

• Guideline
• For each assignment, each student can waive at
  most one question only.
• 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,
• 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

• 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

• Project Type (One of the following)
• Survey
• Implementation-oriented Project
• Research-oriented Project

Your group only needs to read about 23 papers
Your group only needs to read about 12 papers
You can read some papers and conduct research
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Project

• Project Type (One of the following)
• Survey
• Implementation-oriented Project
• Research-oriented Project

• Proposal
• Presentation
• Final report

• Proposal
• Presentation
• Final report
• Coding

• Proposal
• Presentation
• Final report (containing your proposed
  methodology)
• Coding (if any)

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Project

• Project Topic
• 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

• You are allowed to bring a calculator with you.
• Please remember to prepare a calculator for the
  exam

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Major Topics

• Association
• Clustering
• Classification
• Data Warehouse
• Data Mining over Data Streams
• Web Databases
• Multi-criteria Decision Making

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1. Association
We are interested in the items/itemsets with
frequency gt 2
Frequent Pattern (or Frequent Item)
Frequent Pattern (or Frequent Item)
Frequent Pattern (or Frequent Itemset)
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1. Association
We are interested in the items/itemsets with
frequency gt 2
Association Rule1. Apple ? Orange(
customers who buy apple will probably buy
orange.) 2. Orange ? Apple(
customer who buy orange will probably buy apple.)
100
2
3
67
2
2
Problem to find all frequent patterns and
association rules
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Major Topics

• Association
• Clustering
• Classification
• Data Warehouse
• Data Mining over Data Streams
• Web Databases
• Multi-criteria Decision Making

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2. Clustering
Cluster 2(e.g. High Score in Historyand Low
Score in Computer)
Cluster 1(e.g. High Score in Computer and Low
Score in History)
Problem to find all clusters
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Major Topics

• 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.
Decision tree
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Major Topics

• 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)
Data Warehouse
Users
Databases
Pre-computed results
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Major Topics

• 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

• Association
• Clustering
• Classification

Output (Data Mining Results)
Static Data
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5. Data Mining over Data Streams

• Association
• Clustering
• Classification

Output (Data Mining Results)

Unbounded Data
Real-time Processing
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Major Topics

• 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

• 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
Suppose we compare hotel a and hotel b

• We know that hotel a is better
• than hotel b
• because
• Price of hotel a is smaller
• 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
Suppose we compare hotel a and hotel c

• We cannot determine hotel a is better
• than hotel c (wrt two attributes).
• We cannot determine hotel c is better
• than hotel a (wrt two attributes)..
• This is because
• Price of hotel a is smaller
• Distance of hotel c is smaller

We have two attributes. Which hotel should we
select?