Data Mining

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Data Mining

• David Klein Adam Cogan

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Admin Stuff

• Attendance
• You initial sheet
• Hands On Lab
• You get me to initial sheet
• Certificate
• At end of 10 sessions
• If I say you have completed successfully ?

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About

• David Klein is a Senior Software Architect at
  SSW, specialising in .NET SQL Server BI
  solutions
• Current Clients Sally Knox Medical Pisces
• Adam Cogan is Chief Architect at SSW and one of 2
  Microsoft Regional Directors in Australia,
  specialising in Office, SQL and .NET solutions

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Course Overview

• The 5 Sessions (Part B)
• SSIS and Creating a Data Warehouse
• Creating a Cube and Cube Issues
• Reporting Services
• Other Cube Browsers
• Data Mining
• http//www.ssw.com.au/ssw/events/2006SQL/

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Session 5 Tonights Agenda

• Why Data Mining?
• Uses
• Algorithms
• Implementation
• Sql Server Management Studio SSMS
• Reporting Services
• Sql Server Integration Services
• DMX
• Demo ?
• Hands on Lab

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Why Data Mining?

• Marketing
• Who picks the movie? The kids, the wife, me.
• Who are our Customers and what sort of films do
  they hire?
• Is a 30 year old woman with 2 children going to
  hire Arnies latest film
• Validation
• Is this data sensible? Terminator 2 and Toy Story
• Prediction
• Sales Next Year

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Complete Set Of Algorithms
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Naïve Bayes

• Quickly builds mining models that can be used for
  classification and prediction
• It calculates probabilities for each possible
  state of the input attribute, given each state of
  the predictable attribute
• This can later be used to predict an outcome of
  the predicted attribute based on the known input
  attributes
• This makes the model a good option for exploring
  the data

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Naïve Bayes Toy Story 2
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Decision Trees (1)

• Decision Trees assign (classify) each case to one
  of a few (discrete) broad categories of selected
  attribute (variable) and explains the
  classification with few selected input variables
• The process of building is recursive partitioning
  splitting data into partitions and then
  splitting it up more
• Initially all cases are in one big box

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Decision Trees (2)

• The algorithm tries all possible breaks in
  classes using all possible values of each input
  attribute it then selects the split that
  partitions data to the purest classes of the
  searched variable
• Several measures of purity
• Then it repeats splitting for each new class
• Again testing all possible breaks
• Unuseful branches of the tree can be pre-pruned
  or post-pruned

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Decision Trees (3)

• Decision trees are used for classification and
  prediction
• Typical questions
• Predict which customers will leave
• Help in mailing and promotion campaigns
• Explain reasons for a decision
• What are the movies young female customers like
  to buy?

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Decision Trees Who Decides
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Cluster Analysis (1)

• Grouping data into clusters
• Objects within a cluster have high similarity
  based on the attribute values
• The class label of each object is not known
• Several techniques
• Partitioning methods
• Hierarchical methods
• Density based methods
• Model based methods
• And more

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Cluster Analysis (2)

• Segments a heterogeneous population into a number
  of more homogenous subgroups or clusters
• Some typical questions
• Discover distinct groups of customers
• Identification of groups of houses in a city
• In biology, derive animal and plant taxonomies
• Find outliers

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Conclusion When To Use What
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Summary

• Why Data Mining?
• Uses
• Algorithms
• Implementation
• Sql Server Management Studio SSMS
• Reporting Services
• Sql Server Integration Services
• DMX
• Demo ?
• Hands on Lab

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Book

• Data Mining with SQL Server 2005
• ZhaoHui Tang and Jamie MacLennan
• Wiley Press

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2 things
DavidKlein_at_ssw.com.au AdamCogan_at_ssw.com.au
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Thank You!
BI is Cool