Intelligent Data Analysis IDA

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Intelligent Data Analysis (IDA)

• by
• Josipa Kern, PhD
• Andrija Stampar School of Public Health
• Medical School University of Zagreb
• Zagreb, Croatia

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Interest and Excitement for Intelligent Data
Analysis

• Decision making is asking for information and
  knowledge
• Data processing can give them
• Multidimensionality of problems is looking for
  methods for adequate and deep data processing and
  analysis

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Learning Objectives

• To understand the concept of the IDA
• To meet web-sites and literature on IDA
• To meet some tools for IDA
• To learn how to use IDA tools and to validate the
  IDA results

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Performance Objectives

• Recognize problems asking for IDA
• Preparing data and making analysis
• Validating and interpreting results of IDA

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IDA is

• an interdisciplinary study concerned with the
  effective analysis of data
• used for extracting useful information from
  large quantities of online data extracting
  desirable knowledge or interesting patterns from
  existing databases

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IDA or

• Data mining
• Knowledge acquisition from data
• Genetic algorithm-based rule discovery
• Knowledge discovery
• Learning classifier system
• Machine learning
• etc.

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IDA gives knowledge
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Knowledge is

• the distillation of information that has been
  collected, classified, organized, integrated,
  abstracted and value-added
• at a level of abstraction higher than the data,
  and information on which it is based and can be
  used to deduce new information and new knowledge
• usually in the context of human expertise used in
  solving problems.

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Knowledge acquisition

• The process of eliciting, analyzing,
  transforming, classifying, organizing and
  integrating knowledge and representing that
  knowledge in a form that can be used in a
  computer system.

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Knowledge in a domain can be expressed as a
number of rules
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Rule is

• A formal way of specifying a recommendation,
  directive, or strategy, expressed as "IF premise
  THEN conclusion" or "IF condition THEN action".

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How to discover rules hidden in the data?
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Some tools for IDA

• See5 - program for analyzing data and generating
  classifiers in the form of decision trees and/or
  rule sets.
• http//www.rulequest.com

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Some tools for IDA

• Cubist - analyzes data and generates rule-based
  piecewise linear models collections of rules,
  each with an associated linear expression for
  computing a target value..
• http//www.rulequest.com

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Some tools for IDA

• ILLM - the tool constructs classification models
  in the form of rules which represent knowledge
  about relations hidden in data.
• http//dms.irb.hr

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Some tools for IDA

• Magnum Opus - finds association rules providing
  competitive advantage by revealing underlying
  interactions between factors within the data. 
• http//www.rulequest.com

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Evaluation of IDA results

• Absolute relative accuracy
• Sensitivity specificity
• False positive false negative
• Error rate
• Reliability of rules
• Etc.

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Example of IDA

• Illustration of IDA by using See5

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See5application

• application.names - lists the classes to which
  cases may belong and the attributes used to
  describe each case.
• Attributes are of two types discrete attributes
  have a value drawn from a set of possibilities,
  and continuous attributes have numeric values.

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See5application

• application.data - provides information on the
  training cases from which See5 will extract
  patterns.
• The entry for each case consists of one or more
  lines that give the values for all attributes.

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See5application

• application.test - provides information on the
  test cases (used for evaluation of results).
• The entry for each case consists of one or more
  lines that give the values for all attributes.

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See5applicationexample

• Epidemiological study (1970-1990)
• Sample of examinees died from cardiovascular
  diseases during the period
• Question Did they know they were ill?
• 1 they were healthy
• 2 they were ill (drug treatment, positive
  clinical and laboratory findings)

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See5applicationexample

• application.names example
• Goal.
• genderM,F
• activity1,2,3
• age continuous
• smoking No,Yes
• Goal1,2

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See5applicationexample

• application.data example
• M,1,59,Yes,0,0,0,0,119,73,103,86,247,87,15979,?,?,
  ?,1,73,2.5
• M,1,66,Yes,0,0,0,0,132,81,183,239,?,783,14403,2722
  1,19153,23187,1,73,2.6
• M,1,61,No,0,0,0,0,130,79,148,86,209,115,21719,1232
  4,10593,11458,1,74,2.5

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See5applicationexample

• Results example
•  
• Rule 1 (cover 26)
• gender M
• SBP gt 111
• oil_fat gt 2.9
• -gt class 1 0.929

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See5applicationexample

• Results example
•  
• Rule 4 (cover 14)
• smoking Yes
• SBP gt 131
• glucose gt 93
• glucose lt 118
• oil_fat lt 2.9
• -gt class 2 0.938

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See5applicationexample

• Results example
•  
• Rule 15 (cover 2)
• SBP lt 111
• oil_fat gt 2.9
• -gt class 2 0.750

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See5applicationexample

• Results example
•  
• Evaluation on training data
• (199 cases)
•   (a) (b) lt-classified as
• ---- ----
• 107 3 (a) class 1
• 17 72 (b) class 2

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See5applicationexample

• Results example (training set)
•  
• Sensitivity0.97
• Specificity0.81

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See5applicationexample

• Results example
•  
• Evaluation on test data
• (73 cases)
•  
• (a) (b) lt-classified as
• ---- ----
• 43 1 (a) class 1
• 3 26 (b) class 2

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See5applicationexample

• Results example (test set)
•  
•  Sensitivity0.98
• Specificity0.90

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All the suggested IDA tools are available at
mentioned URLs, at least as demo version Try
your own IDA Thank you!