1. Welcome! Knowledge Discovery and Data Mining
Qiang Yang
Hong Kong University of Science and Technology
Lecture 1
2019/5/12
Course Introduction

2. Data Mining: An Example
You are a marketing manager for a brokerage company
Problem: Churn is too high (also known as Attrition)
Turnover (after six month introductory period ends) is 40%
Customers receive incentives (average cost: $160) when account is opened
Giving new incentives to everyone who might leave is very expensive (as well as wasteful)
Bringing back a customer after they leave is both difficult and costly
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Course Introduction 2

3. … A Solution
One month before the end of the introductory period is over, predict which customers will leave
If you want to keep a customer that is predicted to churn, offer them something based on their predicted value
The ones that are not predicted to churn need no attention
If you don’t want to keep the customer, do nothing
How can you predict future behavior?
Build models
Test models
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Course Introduction 3

4. Convergence of Three Technologies
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5. Why Now? 1. Increasing Computing Power
Moore’s law doubles computing power every 18 months
Powerful workstations became common
Cost effective servers (SMPs) provide parallel processing to the mass market
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Course Introduction 5

6. 2. Improved Data Collection
Data Collection  Access  Navigation  Mining
The more data the better (usually)
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7. 3. Improved Algorithms (AI + Data Base)
Techniques have often been waiting for computing technology to catch up
Statisticians already doing “manual data mining”
Good machine learning = intelligent application of statistical processes
A lot of data mining research focused on tweaking existing techniques to get small percentage gains
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Course Introduction 7

8. Definition: Predictive Model
A “black box” that makes predictions about the future based on information from the past and present
Large number of inputs usually available
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9. How are Models Built and Used?
View from 20,000 feet:
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10. The Data Mining Process
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11. What the Real World Looks Like
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12. Predictive Models are…
Decision Trees
Nearest Neighbor Classification
Neural Networks
Rule Induction
K-means Clustering
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Course Introduction 12

13. Data Mining is Not ... Data warehousing
SQL / Ad Hoc Queries / Reporting
Software Agents
Online Analytical Processing (OLAP)
Data Visualization
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Course Introduction 13

14. Common Uses of Data Mining
Marketing:
Direct mail marketing
Web site personalization
Fraud Detection
Credit card fraud detection
Science
Bioinformatics
Gene analysis
Web & Text analysis
Google
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15. Course Description Data Mining and Knowledge Discovery Focus:
Focus 1: Theoretical foundations in Pattern Recognition and Machine Learning
Algorithms:
Differences?
where they apply?
Focus 2: Broad survey of recent research
Focus 3: Hands-on, apply algorithms to KDD data sets
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Course Introduction

16. Topic 1: Foundations Classification algorithms Clustering algorithms
Association algorithms
Sequential Data Mining
Novel Applications
Web
Customer Relationship Management
Biological Data
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17. Topic 2: Hands On Apply learned algorithms to selected data sets
Get familiar with existing software packages and libraries
Final Project will involve working with some datasets
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18. Prerequisites Statistics and Probability would help,
but not necessary
Pattern Recognition would help,
Databases
Knowledge of SQL and relational algebra
But not necessary
One programming language
One of Java, C++, Perl, Matlab, etc.
Will need to read Java Library
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19. Grading Grade Distribution: Assignments (30%) Midterm Exam: 30%
Paper Presentation and Presentation: 40%
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Course Introduction