Welcome! Knowledge Discovery and Data Mining

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Welcome! Knowledge Discovery and Data Mining

• Qiang Yang
• Hong Kong University of Science and Technology
• qyang_at_cs.ust.hk
• http//www.cs.ust.hk

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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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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 dont want to keep the customer, do
  nothing
• How can you predict future behavior?
• Build models
• Test models

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Convergence of Three Technologies
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Why Now? 1. Increasing Computing Power

• Moores 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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2. Improved Data Collection

• Data Collection ? Access ? Navigation ? Mining
• The more data the better (usually)

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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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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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How are Models Built and Used?

• View from 20,000 feet

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The Data Mining Process
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What the Real World Looks Like
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Predictive Models are

• Decision Trees
• Nearest Neighbor Classification
• Neural Networks
• Rule Induction
• K-means Clustering

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Data Mining is Not ...

• Data warehousing
• SQL / Ad Hoc Queries / Reporting
• Software Agents
• Online Analytical Processing (OLAP)
• Data Visualization

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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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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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Topic 1 Foundations

• Classification algorithms
• Clustering algorithms
• Association algorithms
• Sequential Data Mining
• Novel Applications
• Web
• Customer Relationship Management
• Biological Data

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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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Prerequisites

• Statistics and Probability would help,
• but not necessary
• Pattern Recognition would help,
• but not necessary
• 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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Grading

• Grade Distribution
• Assignments (30)
• Midterm Exam 30
• Paper Presentation and Presentation 40