An Excelbased Data Mining Tool

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• An Excel-based Data Mining Tool
• iDA

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ESX A Multipurpose Tool for Data Mining
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The Algorithmic Logic Behind ESX

• Given
• A set of existing concept-level nodes C1, ..., Cn
• An average class resemblance score S
• A new instance I to be classified
• Classify I with the concept class that will
  improve S the most, or hurt S the least.
• If learning is unsupervised, create a new concept
  node with I alone if it results in a better S
  score.

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iDAV Format for Data Mining

• iDA attribute/value format
• First row attribute names
• Second row attribute type identifier
• C categorical, R real (real stands for any
  numeric field)
• Third row attribute usage identifier
• I input, O output, U unused D display only
• Forth row test set data

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A Five-step Approach for Unsupervised Clustering

• Step 1 Enter the Data to be Mined
• Step 2 Perform a Data Mining Session
• Step 3 Read and Interpret Summary Results
• Step 4 Read and Interpret Individual Class
  Results
• Step 5 Visualize Individual Class Rules

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Step 1 Enter The Data To Be Mined
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Step 2 Perform A Data Mining Session

• iDA -gt begin mining session
• Select instance similarity and real-valued
  tolerance setting

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RuleMaker Settings
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Step 3 Read and Interpret Summary Results

• Class Resemblance Scores
• Similarity of instances in the class
• Domain Resemblance Score
• Similarity of instances in the entire set
• Cluster Quality
• Class resemblance with reference to domain
  resemblance (clusters should be at least as good
  as the domain)

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Step 3 Results about Attributes

• Categorical
• Domain Predictability
• Given categorical attribute A with possible
  values v1,..,vn, domain predictability gives the
  number of instances that has A equal to vi (if
  domain predictability score is close to 100,
  most of the instances have the same value, and
  the attribute is not very valuable for learning
  purposes)
• Numeric
• Attribute significance
• Given attribute A, find the range of class means,
  and divide by the domain standard deviation
  (higher values are better for differentiation
  purposes)

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Step 4 Read and Interpret Individual Class
Results

• Class Predictability is a within-class measure.
  
• Given class C and categorical attribute A with
  possible values v1,..,vn, class predictability
  gives the percent of instances that has A equal
  to vi in C
• Class Predictiveness is a between-class measure.
• Given class C and categorical attribute A with
  possible values v1,..,vn, class predictiveness
  for vi is the probability that an instance
  belongs to C given it has value vi for A.

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Necessary and Sufficient Conditions

• A predictiveness score of 1.0 tells us that all
  instances with the particular attribute value
  belong to this particular class.
• gt Attribute v is a sufficient condition for
  membership in this class.
• A predictability score of 1.0 tells us that all
  the instances in this class have Attribute v.
• gt Attribute v is a necessary condition for
  membership in this class.

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Necessary and/or Sufficient Conditions

• If both predictability and predictiveness scores
  are 1.0, the particular value for the attribute
  is necessary and sufficient for class membership.
• ESX outputs necessary and sufficient attribute
  values that meet a particular cut-off (0.80) as
  highly necessary and highly sufficient.

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Step 5 Visualize Individual Class Rules
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RuleMaker Settings

• Recall that we used the setting to ask RuleMaker
  to generate all rules. This is a good way to
  learn about the nature of the problem at hand.

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A Six-Step Approach for Supervised Learning

• Step 1 Choose an Output Attribute
• Step 2 Perform the Mining Session
• Step 3 Read and Interpret Summary Results
• Step 4 Read and Interpret Test Set Results
• Step 5 Read and Interpret Class Results
• Step 6 Visualize and Interpret Class Rules

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Perform the Mining Session

• Decide on the size of the training set.
• The remaining items will be used by the software
  to test the model that is developed (and
  evaluation results will be reported).

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Read and Interpret Summary Results

• The worksheet RES SUM contains summary
  information.
• Class resemblance scores, attribute summary
  information (categorical and numerical) and most
  commonly occurring attributes for each class are
  given.

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Read and Interpret Test Set Results
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Read and Interpret Test Set Results

• Worksheets RES TST, RES MTX
• Reports performance on the test set (which was
  not part of model training)
• RES MTX reports confusion matrix
• RES TST reports for each instance in the test set
  the models classification, and whether it is
  accurate or not.

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Read and Interpret Class Results

• As individual clusters are of interest in
  unsupervised learning, the information about
  individual classes is relevant in supervised
  learning.
• Worksheet RES CLS contains the information.
• Most and least typical instances are also given
  here.
• The worksheet RUL TYP gives typicality scores
  for all of the instances in the test set.

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Visualize and Interpret Class Rules

• All rules or covering set of rules?
• Worksheet RES Rul contains the rules generated by
  RuleMaker
• If all rules are generated, there might be
  overlapping coverage.
• The covering set algorithm works iteratively, by
  identifying the best covering rule and updating
  the instance set to be covered.
• It is possible to run RuleMaker without running
  the mining algorithm again. This menu item can be
  used to change the RuleMaker settings to generate
  alternative rule sets.

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Generating Rules The General Idea

• Choose an attribute that differentiates all
  domain/subclass instances best.
• Use the attribute to subdivide instances into
  classes.
• For each subclass
• If the instances meet a predefined criteria,
  generate a defining rule for the subclass.
• If the predefined criteria is not met, go to Step
  1.

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Techniques for Generating Rules

• Define the scope of the rules.
• Choose the instances.
• Set the minimum rule correctness.
• Define the minimum rule coverage.
• Choose an attribute significance value.

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Instance Typicality

• Typicality Scores
• Identify prototypical and outlier instances.
• Select a best set of training instances.
• Used to compute individual instance
  classification confidence scores.

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Special Considerations and Features

• Avoid Mining Delays
• The Quick Mine Feature
• Erroneous and Missing Data