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Page Number: 1 Datamining in e-Business: Veni, Vidi, Vici! by Prof. Dr. Veljko Milutinovic.

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Presentation on theme: "Page Number: 1 Datamining in e-Business: Veni, Vidi, Vici! by Prof. Dr. Veljko Milutinovic."— Presentation transcript:

1 Page Number: 1 Datamining in e-Business: Veni, Vidi, Vici! by Prof. Dr. Veljko Milutinovic

2 Page Number: 2 THIS IS A DEMO VERSION OF THE TUTORIAL IN DATAMINING FOR E-BUSINESS ONLY A FEW SLIDES OF THE ORIGINAL TUTORIAL ARE PRESENTED HERE

3 Page Number: 3 Focus of this Presentation Focus of this Presentation  Data Mining problem types  Data Mining models and algorithms  Efficient Data Mining  Available software

4 Page Number: 4 Decision Trees Balance>10Balance<=10 Age<=32Age>32 Married=NOMarried=YES

5 Page Number: 5 Decision Trees Decision Trees

6 Page Number: 6 Rule Induction Rule Induction  Method of deriving a set of rules to classify cases  Creates independent rules that are unlikely to form a tree  Rules may not cover all possible situations  Rules may sometimes conflict in a prediction

7 Page Number: 7 Comparison of fourteen DM tools Evaluated by four undergraduates inexperienced at data mining, a relatively experienced graduate student, and a professional data mining consultant Evaluated by four undergraduates inexperienced at data mining, a relatively experienced graduate student, and a professional data mining consultant Run under the MS Windows 95, MS Windows NT, Macintosh System 7.5 Run under the MS Windows 95, MS Windows NT, Macintosh System 7.5 Use one of the four technologies: Decision Trees, Rule Inductions, Neural, or Polynomial Networks Use one of the four technologies: Decision Trees, Rule Inductions, Neural, or Polynomial Networks Solve two binary classification problems: multi-class classification and noiseless estimation problem Solve two binary classification problems: multi-class classification and noiseless estimation problem Price from 75$ to $ Price from 75$ to $

8 Page Number: 8 Comparison of fourteen DM tools The Decision Tree products were - CART - Scenario - See5 - S-Plus The Decision Tree products were - CART - Scenario - See5 - S-Plus The Rule Induction tools were - WizWhy - DataMind - DMSK The Rule Induction tools were - WizWhy - DataMind - DMSK Neural Networks were built from three programs - NeuroShell2 - PcOLPARS - PRW Neural Networks were built from three programs - NeuroShell2 - PcOLPARS - PRW The Polynomial Network tools were - ModelQuest Expert - Gnosis - a module of NeuroShell2 - KnowledgeMiner The Polynomial Network tools were - ModelQuest Expert - Gnosis - a module of NeuroShell2 - KnowledgeMiner

9 Page Number: 9 Criteria for evaluating DM tools A list of 20 criteria for evaluating DM tools, put into 4 categories: Capability measures what a desktop tool can do, and how well it does it - Handles missing data- - Considers misclassification costs - Allows data transformations - Includes quality of tesing options - Has a programming language - Provides useful output reports - Provides visualisation Capability measures what a desktop tool can do, and how well it does it - Handles missing data- - Considers misclassification costs - Allows data transformations - Includes quality of tesing options - Has a programming language - Provides useful output reports - Provides visualisation

10 Page Number: 10 Criteria for evaluating DM tools Interoperability shows a tool’s ability to interface with other computer applications - Importing data - Exporting data - Links to other applications Interoperability shows a tool’s ability to interface with other computer applications - Importing data - Exporting data - Links to other applications Flexibility - Model adjustment flexibility - Customizable work enviroment - Ability to write or change code Flexibility - Model adjustment flexibility - Customizable work enviroment - Ability to write or change code


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