Download - Data Mining Applications
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Business Applications of Data Mining
By: Team 1
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Data Mining – An Introduction
Discovery New Information Patterns Rules
Extensive Data
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Data Mining – Goals
Prediction e.g. What consumers will buy under certain discounts
Identification e.g. Gene identified by certain DNA sequence
Classification e.g. Health foods, party foods, school lunch foods
Optimization e.g. Maximizing sales and profit
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Data Mining – Types of Info.
Association Rules When shopper buys A, likely to buy B
Classification Hierarchies Mutual funds judged by growth, income, stability
Sequential Patterns Association over time
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Types of Info, Continued
Patterns within Time Series Two products sell same in summer, not in winter
Clustering Medication grouped together based on side effects
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Marketing
Theory for Data Mining in Marketing
Benefits in Marketing
Segmentation
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Marketing
Optimized Segmentation
Client Applications
MCI’s Friends and Family
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Data Mining in Health Care
Why use Data Mining? What does it do? Who will use it? Who will benefit? Examples of real world applications
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Why use Data Mining?
Demand for quality health care is rising Need more affordable health care Consolidation of industry = more data Data Mining Tools are here NOW
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What does it do?
Discovers patterns Finds hidden correlation Gives the user information needed to
make the best decision
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Who will use it?
Hospitals Clinics Outpatient Centers Pharmacies It will create a company standard, which
will then create an industry standard
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Who will benefit?
Chief Medical Officer enterprise reporting
Financial Analyst resource consumption in the organization
Physician access to patient health history
Patients faster, safer, cheaper, health care
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Pneumonia Deaths
Death rate was 12% Average stay two weeks Discovered problem between doctors
and the lab Death rate now 9% Average stay is 5 days
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Detecting Insurance Fraud
Devise rules, example: Ambulance trip with no medical services
Help investigators use time more efficiently
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Health Care Wrap-Up
New knowledge will not be discovered by the program, only by the user
Industry will save money and become more efficient
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Manufacturing
Key Applications: Design and Analysis of Experiments
Reliability Analysis and Life Expectancy
Field Failure Analysis and Reporting
Supply Chain Optimization
Demand Forecasting, Optimization and Reporting
Statistical Process Control/Six Sigma INSIGHTFUL MANUFACTURING SOLUTIONShttp://www.insightful.com/industry/manufacturing/default.asp
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Manufacturing
CRISP-DM- Real world practical
input as base for creation
CRISP process:- Business Understanding - Data Understanding - Data Preparation - Modeling- Evaluation - Deployment
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Manufacturing
HP and Motorola chips- Semiconductor yield enhancement: 10x faster than standard approaches, yield increases ranged from 3% to 15% - Manufacturing optimization
Tin platted Steel- 90% of product used for food shipping
- Excess coating reduced 30%, savings $.6Mil
- 95% of cases, error of less than 10% in prediction
- Actual results: 99.7% of the cases showing good performance
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Manufacturing
CRISP-DM HP chips Motorola chips
- Semiconductor yield enhancement
- Manufacturing optimization
Tin platted Steel
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Manufacturing
Future: CALD research- Autonomous Decision-Making Systems
* Autonomy (get rid of experiment-design-trained statistician). * Minimizing the number of expensive experiments. * Optimizing the expected value, given very noisy evaluations.
3M and a large U.S. food processing company experience financial savings
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Works Cited Abajo, Nicols, et al. "ANN Quality Diagnostic Models for Packaging
Manufacturing: An Industrial Data Mining Case Study." KDD '04: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, WA, USA, <http://doi.acm.org/10.1145/1014052.1016917>.
Apte, Chidanand, et al. "Business Applications of Data Mining." Communications of the ACM 45.8 (2002): 49-53. <http://doi.acm.org/10.1145/545151.545178>.
Course Text Hirji, Karim K. "Exploring Data Mining Implementation." Communications
of the ACM 44.7 (2001): 87-93. <http://doi.acm.org/10.1145/379300.379323>.
Silver, Michael, et al. "Case Study: How to Apply Data Mining Techniques in a Healthcare Data Warehouse." Journal of Healthcare Information
Management 15.2 (2001): 155-64.