DataMining

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DataMining

• By
• Guan Hang Su
• CS157A section 2 fall 2005

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Outline

• Overview
• ---- Define Data Mining
• ---- Foundation of Data Mining
• ---- Scope of Data Mining
• ---- Techniques in data mining
• ----Applications

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What is DataMining?

• Discovering hidden value in your data
  warehouse
  

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Define Data Mining

• The automated extraction of hidden predictive
  information from (large) databases
• Three key words
• Automated
• Hidden
• Predictive
• Implicit is a statistical methodology
• Data mining lets you be proactive
• Prospective rather than Retrospective

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The Foundations of Data Mining

• Data mining techniques are the result of a long
  process of research and product development. This
  evolution began when business data was first
  stored on computers, continued with improvements
  in data access, and more recently, generated
  technologies that allow users to navigate through
  their data in real time. Data mining takes this
  evolutionary process beyond retrospective data
  access and navigation to prospective and
  proactive information delivery.

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The Foundations of Data Mining (continue)

• Data mining is ready for application in
• the business community because it is supported
  by three technologies that are now sufficiently
  mature
• Massive data collection
• Powerful multiprocessor computers
• Data mining algorithms

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The Scope of Data Mining

• Data mining derives its name from the
  similarities between searching for valuable
  business information in a large database
• Example finding linked products in
  gigabytes of store scanner data and mining a
  mountain for a vein of valuable ore.
• Both processes require either sifting
  through an immense amount of material, or
  intelligently probing it to find exactly where
  the value resides.

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The Scope of Data Mining (cont..)

• Given databases of sufficient size and
  quality, data mining technology can generate new
  business opportunities by providing these
  capabilities
• Automated prediction of trends and behaviors
• Automated discovery of previously unknown
  patterns.

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The Scope of Data Mining (cont..)

• Automated prediction of trends and behaviors
• --- Data mining automates the process of
  finding predictive information in large
  databases. Questions that traditionally required
  extensive hands-on analysis can now be answered
  directly from the data.
• Typical example of a predictive problem
  1)targeted marketing.
• 2) forecasting bankruptcy

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The Scope of Data Mining (cont..)

• Automated discovery of previously unknown
  patterns
• ---- Data mining tools sweep through databases
  and identify previously hidden patterns in one
  step.
• Example of pattern discovery The analysis of
  retail sales data to identify seemingly unrelated
  products that are often purchased together
• Other pattern discovery problems include
  detecting fraudulent credit card transactions and
  identifying anomalous data that could represent
  data entry keying errors.

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Techniques in data mining

• The most commonly used techniques in data mining
• Artificial neural networks
• Decision trees
• Genetic algorithms
• Nearest neighbor method
• Rule induction

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• Artificial neural networks Non-linear predictive
  models that learn through training and resemble
  biological neural networks in structure.
• Decision trees Tree-shaped structures that
  represent sets of decisions. These decisions
  generate rules for the classification of a
  dataset. Specific decision tree methods include
  Classification and Regression Trees (CART) and
  Chi Square Automatic Interaction Detection
  (CHAID)

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• Genetic algorithms Optimization techniques that
  use processes such as genetic combination,
  mutation, and natural selection in a design based
  on the concepts of evolution.
• Nearest Neighbor. A data mining technique that
  performs prediction by finding the prediction
  value of records (near neighbors) similar to the
  record to be predicted.

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• Rule induction The extraction of useful if-then
  rules from data based on statistical significance
  
• Other Techniques
• Bayesian networks
• ----- Naïve Bayes
• Support vector machines
• Many more..

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

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Example of Neural Network

• Difficult interpretation
• Tends to overfit the data
• Extensive amount of training time
• A lot of data preparation
• Works with all data types

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Example of Rule of induction

• Description
• Produces decision trees
• income lt 40K
• job gt 5 yrs then good risk
• job lt 5 yrs then bad risk
• income gt 40K
• high debt then bad risk
• low debt then good risk
• Or Rule Sets
• Rule 1 for good risk
• if income gt 40K
• if low debt
• Rule 2 for good risk
• if income lt 40K
• if job gt 5 years

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K-Nearest-Neighbor (kNN) Models

• Use entire training database as the model
• Find nearest data point and do the same thing as
  you did for that record

100
Age
0 Doses 1000
Very easy to implement. More difficult to use in
production. Disadvantage Huge Models
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Example of Decision Trees
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How Data Mining Works

• How exactly is data mining able to tell you
  important things that you didn't know or what is
  going to happen next? The technique that is used
  to perform these feats in data mining is called
  modeling.
• Modeling is simply the act of building a model in
  one situation where you know the answer and then
  applying it to another situation that you don't.

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• Computers are loaded up with lots of information
  about a variety of situations where an answer is
  known and then the data mining software on the
  computer must run through that data and distill
  the characteristics of the data that should go
  into the model
• Once the model is built it can then be used in
  similar situations where you don't know the
  answer

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Some results of Data Mining

• Forecasting what may happen in the future.
• Classifying people or things into groups by
  recognizing patterns.
• Clustering people or things into groups based on
  their attributes.
• Sequencing what events are likely to lead to
  later events

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Example

• For example, say that you are the director of
  marketing for a telecommunications company and
  you'd like to acquire some new long distance
  phone customers.
• 1)randomly mail out the coupon to general
  population.
• 2) or use your business experience stored in
  your database to build a model , then choose the
  right target.

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Cont..

• As the marketing director you have access to a
  lot of information about all of your customers
  their age, sex, credit history and long distance
  calling usage.
• The problem is that you don't know the long
  distance calling usage of these prospects (since
  they are most likely now customers of your
  competition).

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• We 'd like to concentrate on those prospects who
  have large amounts of long distance usage .We can
  accomplish this by building a model

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• For instance, a simple model for a
  telecommunications company might be
• 98 of my customers who make more than
  60,000/year spend more than 80/month on long
  distance.
• With this model in hand new customers can be
  selectively targeted

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Architecture for Data Mining

• To best apply these advanced techniques, they
  must be fully integrated with a data warehouse as
  well as flexible interactive business analysis
  tools.
• Many data mining tools currently operate outside
  of the warehouse, requiring extra steps for
  extracting, importing, and analyzing the data.
  Furthermore, when new insights require
  operational implementation, integration with the
  warehouse simplifies the application of results
  from data mining.

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• illustrates an architecture for advanced analysis
  in a large data warehouse

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Data Mining Applications

• The US Drug Enforcement Agency needed to be more
  effective in their drug busts.
• Analyzed suspects cell phone usage to focus
  investigations.

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• HSBC need to cross-sell more effectively by
  identifying profiles that would be interested in
  higher yielding investments.
• Reduced direct mail costs by 30 while
  garnering 95 of the campaigns revenue.

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Bibliography

• http//www.thearling.com/dmintro/dmintro_frame.htm
  
• http//www.thearling.com/text/dwhite/dmwhite.htm
• http//www.cs.sjsu.edu/faculty/lee/cs157/25SpL22Da
  taMining.ppt
• http//www.oracle.com/technology/products/bi/odm/i
  ndex.html