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Data Mining and Decision Support Integration

ACAI’05/SEKT’05 ADVANCED COURSE ON KNOWLEDGE DISCOVERY. Data Mining and Decision Support Integration. Marko Bohanec Jo žef Stefan Institut e Department of Knowledge Technologies & University of Ljubljana Faculty of Administration. Decision Support. modeling. model. decision makers+

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Data Mining and Decision Support Integration

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  1. ACAI’05/SEKT’05ADVANCED COURSE ON KNOWLEDGE DISCOVERY Data Mining andDecision SupportIntegration Marko Bohanec Jožef Stefan Institute Department of Knowledge Technologies & University of Ljubljana Faculty of Administration

  2. Decision Support modeling model decision makers+ experts+ decision analysts Data Mining vs. Decision Support Data Mining knowledge discovery from data • Use of models: • classification • clustering • evaluation • analysis • visualization • explanation • ... model data

  3. Overview 1. Decision Support: • Decision problem • Decision-making • Decision support • Decision analysis • Multi-attribute modeling 2. Decision Support and Data Mining • How to combine and integrate DS and DM? • DS for DM • DM for DS • DM, then DS • DS, then DM • DM and DS • DS for DM: ROC space • DM and DS: Combining DEX and HINT

  4. Literature Part I: Basic Technologies • Chapter 3: Decision Support • Chapter 4: Integration ofData Mining and Decision Support Part II: Integration Aspects of DM and DS • Chapter 7: DS for DM: ROC Analysis Part III: Applications of DM and DS • Chapter 15: Five Decision Support Applications • Chapter 16: Large and Tall Buildings • Chapter 17: Educational Planning

  5. Decision SupportDecision ProblemDecision-MakingDecision SupportDecision AnalysisMulti-Attribute Modeling Chapter 3 – M. Bohanec: Decision Support

  6. Decision-Making Decision: The choice of one among a number of alternatives Decision-Making: A process of making the choice that includes: • Assessing the problem • Collecting and verifying information • Identifying alternatives • Anticipating consequences of decisions • Making the choice using sound and logical judgment based on available information • Informing others of decision and rationale • Evaluating decisions

  7. Decision Problem options(alternatives) goals • FIND the option that best satisfies the goals • RANK options according to the goals • ANALYSE, JUSTIFY, EXPLAIN, …, the decision

  8. Types of Decisions • Easy (routine, everyday) vs. Difficult (complex) • One-Time vs. Recurring • One-Stage vs. Sequential • Single Objective vs. Multiple Objectives • Individual vs. Group • Structured vs. Unstructured • Tactical, Operational, Strategic

  9. Characteristics of Complex Decisions • Novelty • Unclearness: Incomplete knowledge about the problem • Uncertainty: Outside events that cannot be controlled • Multiple objectives (possibly conflicting) • Group decision-making • Important consequences of the decision • Limited resources

  10. Human DM Machine DM Decision-Making Decision Systems • Switching circuits • Processors • Computer programs • Systems for routine DM • Autonomous agents • Space probes Decision Sciences

  11. Decision-Making Decision Systems Decision Sciences Normative Descriptive Decision Support Decision Theory Utility Theory Game Theory Theory of Choice  Cognitive Psychology Social and Behavioral Sciences 

  12. Decision Support Decision Support:Methods and tools for supporting people involved in the decision-making process Central Disciplines: • Operations Research and Management Sciences • Decision Analysis • Decision Support Systems Contributing and Related Disciplines: • Decision Sciences (other than DS itself) • Statistics, Applied Mathematics • Computer Sciences:Information Systems, Databases, Data Warehouses, OLAP • Artificial Intelligence: Expert Systems, ML, NN, GA • Knowledge Discovery from Databases and Data Mining Other Methods and Tools: • Representation and visualization tools • Methods and tools for organizing data, facts, thoughts, ... • Communication technology • Mediation systems

  13. OR/MS DA DSS Other Decision-Making Decision Systems Decision Sciences Normative Descriptive Decision Support Decision trees Influence diagrams Multi-attribute models

  14. Decision Analysis Decision Analysis: Applied Decision Theory Provides a framework for analyzing decision problems by • structuring and breaking them down into more manageable parts, • explicitly considering the: • possible alternatives, • available information • uncertainties involved, and • relevant preferences • combining these to arrive at optimal (or "good") decisions

  15. The Decision Analysis Process Identify decision situation and understand objectives Identify alternatives • Decompose and model • problem structure • uncertainty • preferences Sensitivity Analyses Choose best alternative Implement Decision

  16. EVALUATION ANALYSIS Evaluation Models options EVALUATION MODEL

  17. Decision Trees Multi-Attribute Utility Models Succeed Invest Fail Investment Do not invest Costs Risks Results Influence Diagrams Invest? Success? Analytic Hierarchy Process Return Types of Models in Decision Analysis

  18. Multi-Attribute Models cars buying maint PRICE safety CAR doors TECH COMF pers lug problem decomposition

  19. PRICE TECH.CHAR. BUYING MAINTEN SAFETY COMFORT Tree of Attributes Decomposition of the problem to sub-problems ("Divide and Conquer!") CAR The most difficult stage!

  20. SAFETY COMFORT TECH.CH. low exc unacc high low unacc med accept accept high good exc PRICE TECH.CHAR. 75% 25% BUYING MAINTEN SAFETY COMFORT Utility Functions (Aggregation) Aggregation: bottom-up aggregation of attributes’ values CAR

  21. EVALUATION Evaluation and Analysis • direction: bottom-up(terminal  root attributes) • result: each option evaluated • inaccurate/uncertain data?

  22. ANALYSIS Evaluation and Analysis • interactive inspection • “what-if” analysis • sensitivity analysis • explanation

  23. DEXi: Computer Program forMulti-Attribute Decision Making • Creation and editing of • model structure (tree of attributes) • value scales of attributes • decision rules (incl. using weights) • options and their descriptions (data) • Evaluation of options(can handle missing values) • “What-if” analysis • Reporting: • tables • charts http://www-ai.ijs.si/MarkoBohanec/dexi.html

  24. Some Application Areas • INFORMATION TECHNOLOGY • evaluation of computers • evaluation of software • evaluation of Web portals • PROJECTS • evaluation of projects • evaluation of proposal and investments • product portfolio evaluation • COMPANIES • business partner selection • performance evaluation of companies • PERSONNEL MANAGEMENT • personnel evaluation • selection and composition of expert groups • evaluation of personal applications • educational planning • MEDICINE and HEALTH-CARE • risk assessment • diagnosis and prognosis • OTHER AREAS • assessment of technologies • assessments in ecology and environment • granting personal/corporate loans • choosing sports

  25. Allocation of Housing Loans Ownership Present Suitability Solving Housing Stage Work stage Advantages Earnings Priority Status Maint/Employ Health Family Soc-Health - Age Social Children

  26. Medicine:Breast Cancer Risk Assessment Bohanec, M., Zupan, B., Rajkovič, V.: Applications of qualitative multi-attribute decision models in health care, International Journal of Medical Informatics 58-59, 191-205, 2000.

  27. Evaluation and Analysis of Options

  28. Selective Explanation of Options

  29. Diabetic Foot Risk Assessment Who: • General Hospital Novo Mesto, Slovenia • IJS • Infonet, d.o.o. Why: • Reduce the number of amputations • Improve the risk assessment methodology • Improve the DSS module of clinical information system How: • Develop multi-attribute risk assessment model • Evaluate it on patient data (about 3400 patients) • Integrate into the clinical information system Chapter 15 – M. Bohanec, V. Rajkovič, B. Cestnik: 5 DS Applications

  30. Diabetic Foot Risk Assessment Model Structure

  31. 2. Combining Data Mining and Decision SupportHow to combine DS and DM?DS for DM: ROC spaceDM and DS: Combining DEX and HINT Chapter 4 – N. Lavrač, M. Bohanec: Integration of DM and DS

  32. Decision Support modeling model decision makers+ experts+ decision analysts Data Mining vs. Decision Support Data Mining knowledge discovery from data • Use of models: • classification • clustering • evaluation • analysis • visualization • explanation • ... model data

  33. ? DM + DS Integration ? Data Mining Decision Support

  34. DM + DS Integration !

  35. Combining DM and DS • “DS for DM”: • ROC methodology • meta-learning • “DM for DS”: • MS Analysis Services • model revision (from data) • “DM, then DS” (sequential application): • Decisions-At-Hand approach • “DS, then DM” (sequential application): • using models in data pre-processing for DM • “DM and DS” (parallel application): • combining through models, e.g.,DEXi and HINT • considering different problem dimensions

  36. “DS for DM” Data Mining Decision Support Decision support within the DM processe.g., ROC curves

  37. ROC space • True positive rate = #true pos. / #pos. • TPr1 = 40/50 = 80% • TPr2 = 30/50 = 60% • False positive rate = #false pos. / #neg. • FPr1 = 10/50 = 20% • FPr2 = 0/50 = 0% • ROC space has • FPr on X axis • TPr on Y axis Chapter 7 – Slides by Peter Flach

  38. true positive rate false positive rate The ROC convex hull

  39. true positive rate false positive rate The ROC convex hull

  40. true positive rate false positive rate Choosing a classifier

  41. true positive rate false positive rate Choosing a classifier

  42. “DM for DS” Data Mining Decision Support • Introducing DM methods into the DS process: • MS SQL Server - Analysis Services • model revision

  43. “DM for DS”: Model Revision

  44. Sequential Application:“First DS, thenDM” Decision Support DataMining Model 1 Model 2

  45. “First DS, then DM”in Data Pre-Processing Input attributes Generated attributes

  46. Sequential Application:“First DM, thenDS” DataMining Decision Support Model 1 Model 2

  47. Decision Model in XML Decisions-At-Hand Schema Decision Support Shells … … on Palm Data Mining (Model Construction) … on the Web (Synchronization or Upload) Blaž Zupan et al.: http://www.ailab.si/app/palm/

  48. Model Common modeling formalism “DM and DS”Through Model Development Requirements Expertise Expertise Data Data Data Mining Decision Support Model Chapter 4+ references

  49. Multi-Attribute Decision Models Expertise Data HINT DEX Data Mining Decision Support Model Qualitative Hierarchical Multi-Attribute Decision Models

  50. Decomposition of the problem to less complex subproblems Qualitative attributes Decision rules CAR PRICE TECH buying maint safety COMFORT doors pers lug Model 1. Qualitative Multi-Attribute Models

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